diff --git a/README.md b/README.md
index 4fe0e73..5c1ec45 100644
--- a/README.md
+++ b/README.md
@@ -2,28 +2,11 @@
 
 
 # MS2Query 2.0
-more to come...
-
-## Basic workflow (so far):
-
-### Library generation
-```python
-from ms2query.create_new_library import create_new_library
-
-ms2query_lib = create_new_library(
-    spectra_files=["spectra.mgf"],
-    annotation_files=[],
-    output_folder="my_ms2query_folder/",
-    model_path="models/ms2deepscore.pt"
-)
-```
-
-### Loading already generated library
-```python
-from ms2query.create_new_library import load_created_library
-
-lib = load_created_library("my_ms2query_folder/")
-```
+A first basic implementation is out now, more to follow soon...
+The new MS2Query appraoch has a higher accuracy and has a much simpler and faster underlying algorithm. We will hopefully soon share a first preprint as well, showing all the benchmarking. 
 
+The current runably version still requires to create the library files, which takes some time for the first run. 
 
+Soon this will be much easier and faster. We will add downloadable precomputed files, make MS2Query pip installable, add a database and allow faster MS2DeepScore searching. 
 
+The tutorial for the current prototype can be found in notebooks/tutorial. 
diff --git a/ms2query/benchmarking/reference_methods/readme.md b/ms2query/benchmarking/reference_methods/readme.md
deleted file mode 100644
index 18eab2f..0000000
--- a/ms2query/benchmarking/reference_methods/readme.md
+++ /dev/null
@@ -1 +0,0 @@
-All files here are not important to core functionality and in fact not used, but might be nice to use for future testing. They are currently not yet used in the notebooks. Once they are used there they can probably be removed here.
diff --git a/ms2query/benchmarking/AnnotatedSpectrumSet.py b/ms2query/ms2query_development/AnnotatedSpectrumSet.py
similarity index 99%
rename from ms2query/benchmarking/AnnotatedSpectrumSet.py
rename to ms2query/ms2query_development/AnnotatedSpectrumSet.py
index 93ff130..bda0601 100644
--- a/ms2query/benchmarking/AnnotatedSpectrumSet.py
+++ b/ms2query/ms2query_development/AnnotatedSpectrumSet.py
@@ -6,7 +6,7 @@
 from matchms.importing import load_spectra
 from ms2deepscore.models import SiameseSpectralModel
 from tqdm import tqdm
-from ms2query.benchmarking.Embeddings import Embeddings
+from ms2query.ms2query_development.Embeddings import Embeddings
 
 
 class AnnotatedSpectrumSet:
diff --git a/ms2query/benchmarking/Embeddings.py b/ms2query/ms2query_development/Embeddings.py
similarity index 100%
rename from ms2query/benchmarking/Embeddings.py
rename to ms2query/ms2query_development/Embeddings.py
diff --git a/ms2query/benchmarking/Fingerprints.py b/ms2query/ms2query_development/Fingerprints.py
similarity index 98%
rename from ms2query/benchmarking/Fingerprints.py
rename to ms2query/ms2query_development/Fingerprints.py
index 4d7a9c1..4e20cce 100644
--- a/ms2query/benchmarking/Fingerprints.py
+++ b/ms2query/ms2query_development/Fingerprints.py
@@ -5,8 +5,8 @@
 from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi
 from numpy.typing import NDArray
 from tqdm import tqdm
-from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet
 from ms2query.metrics import generalized_tanimoto_similarity_matrix
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
 
 
 class Fingerprints:
diff --git a/ms2query/ms2query_development/ReferenceLibrary.py b/ms2query/ms2query_development/ReferenceLibrary.py
new file mode 100644
index 0000000..e12c91c
--- /dev/null
+++ b/ms2query/ms2query_development/ReferenceLibrary.py
@@ -0,0 +1,231 @@
+from collections import defaultdict
+from pathlib import Path
+from typing import Sequence
+import numpy as np
+import pandas as pd
+from matchms.importing import load_spectra
+from matchms.Spectrum import Spectrum
+from ms2deepscore.models import SiameseSpectralModel, load_model
+from ms2deepscore.vector_operations import cosine_similarity_matrix
+from tqdm import tqdm
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.Embeddings import Embeddings, _to_json_serializable
+from ms2query.ms2query_development.Fingerprints import Fingerprints
+from ms2query.ms2query_development.TopKTanimotoScores import TopKTanimotoScores
+
+
+class ReferenceLibrary:
+    # Set default file names to enable save and load per library
+    embedding_file_name = "embeddings.npz"
+    top_k_tanimoto_scores_file_name = "top_k_tanimoto_scores.parquet"
+    reference_metadata_file_name = "library_metadata.parquet"
+    ms2deepscore_model_file_name = "ms2deepscore_model.pt"
+    metadata_to_store = [
+        "precursor_mz",
+        "retention_time",
+        "collision_energy",
+        "compound_name",
+        "smiles",
+        "inchikey",
+    ]
+    fingerprint_type = "daylight"
+    fingerprint_nbits = 4096
+    top_k_inchikeys = 8
+
+    def __init__(
+        self,
+        ms2deepscore_model: SiameseSpectralModel,
+        reference_embeddings: Embeddings,
+        top_k_tanimoto_scores: TopKTanimotoScores,
+        reference_metadata: pd.DataFrame,
+    ):
+        self.ms2deepscore_model = ms2deepscore_model
+        self.reference_embeddings = reference_embeddings
+        self.top_k_tanimoto_scores = top_k_tanimoto_scores
+        self.reference_metadata = reference_metadata
+
+        # Check that the loaded files match
+        if _to_json_serializable(ms2deepscore_model.model_settings.get_dict()) != reference_embeddings.model_settings:
+            raise ValueError(
+                "The settings of the ms2deepscore model do not match the model used for creating the library embeddings"
+            )
+        if list(self.reference_metadata["spectrum_hashes"]) != [
+            str(spectrum_hash) for spectrum_hash in reference_embeddings.index_to_spectrum_hash
+        ]:
+            raise ValueError("The loaded metadata does not match the used embeddings")
+        if {inchikey[:14] for inchikey in reference_metadata["inchikey"]} != set(
+            top_k_tanimoto_scores.top_k_inchikeys_and_scores.index
+        ):
+            raise ValueError("The inchikeys in the metadata and in the top_k_tanimoto_scores do not match")
+
+        # Get the spectrum_indices_per_inchikey
+        self.spectrum_indices_per_inchikey = defaultdict(list)
+        for lib_spec_index, inchikey in enumerate(reference_metadata["inchikey"]):
+            self.spectrum_indices_per_inchikey[inchikey[:14]].append(lib_spec_index)
+
+    @classmethod
+    def load_from_directory(cls, library_file_directory) -> "ReferenceLibrary":
+        reference_embeddings_file = library_file_directory / cls.embedding_file_name
+        top_k_tanimoto_scores_file = library_file_directory / cls.top_k_tanimoto_scores_file_name
+        reference_metadata_file = library_file_directory / cls.reference_metadata_file_name
+        ms2deepscore_model_file_name = library_file_directory / cls.ms2deepscore_model_file_name
+        return cls.load_from_files(
+            ms2deepscore_model_file_name, reference_embeddings_file, top_k_tanimoto_scores_file, reference_metadata_file
+        )
+
+    @classmethod
+    def load_from_files(
+        cls,
+        ms2deepscore_model_file_name,
+        reference_embeddings_file,
+        top_k_tanimoto_scores_file,
+        reference_metadata_file,
+    ) -> "ReferenceLibrary":
+        return cls(
+            load_model(ms2deepscore_model_file_name),
+            Embeddings.load(reference_embeddings_file),
+            TopKTanimotoScores.load(top_k_tanimoto_scores_file),
+            pd.read_parquet(reference_metadata_file),
+        )
+
+    @classmethod
+    def create_from_spectra(
+        cls,
+        library_spectra: Sequence[Spectrum],
+        ms2deepscore_model_file_name: str,
+        store_file_directory=None,
+        store_files=True,
+    ) -> "ReferenceLibrary":
+        """Creates all the files needed for MS2Query and stores them"""
+        if store_file_directory is None:
+            store_file_directory = Path(ms2deepscore_model_file_name).parent
+        else:
+            store_file_directory = Path(store_file_directory)
+        if store_files:
+            # Check the files don't exist yet
+            for file in (
+                store_file_directory / cls.embedding_file_name,
+                store_file_directory / cls.top_k_tanimoto_scores_file_name,
+                store_file_directory / cls.reference_metadata_file_name,
+            ):
+                if file.exists():
+                    raise FileExistsError(f"There is already a file stored with the name {file}")
+
+        # library_spectra = list(tqdm(load_spectra(library_spectra_file), "Loading library spectra"))
+        library_spectrum_set = AnnotatedSpectrumSet.create_spectrum_set(library_spectra)
+        ms2deepscore_model = load_model(ms2deepscore_model_file_name)
+        library_spectrum_set.add_embeddings(ms2deepscore_model)
+
+        fingerprints = Fingerprints.from_spectrum_set(library_spectrum_set, cls.fingerprint_type, cls.fingerprint_nbits)
+        top_k_tanimoto_scores = TopKTanimotoScores.calculate_from_fingerprints(
+            fingerprints, fingerprints, cls.top_k_inchikeys
+        )
+        reference_metadata = extract_metadata_from_library(
+            library_spectrum_set,
+            cls.metadata_to_store,
+        )
+
+        if store_files:
+            reference_metadata.to_parquet(store_file_directory / cls.reference_metadata_file_name)
+            top_k_tanimoto_scores.save(store_file_directory / cls.top_k_tanimoto_scores_file_name)
+            library_spectrum_set.embeddings.save(store_file_directory / cls.embedding_file_name)
+        return cls(ms2deepscore_model, library_spectrum_set.embeddings, top_k_tanimoto_scores, reference_metadata)
+
+    def run_ms2query(
+        self,
+        query_spectra: Sequence[Spectrum],
+        batch_size: int = 1000,
+    ) -> pd.DataFrame:
+
+        query_embeddings = Embeddings.create_from_spectra(query_spectra, self.ms2deepscore_model)
+
+        num_of_query_embeddings = query_embeddings.embeddings.shape[0]
+
+        library_index_highest_ms2deepscore = np.zeros((num_of_query_embeddings), dtype=int)
+        highest_ms2deepscore_values = np.zeros((num_of_query_embeddings), dtype=float)
+        ms2query_scores = []
+
+        for start_idx in tqdm(
+            range(0, num_of_query_embeddings, batch_size),
+            desc="Predicting highest ms2deepscore per batch of "
+            + str(min(batch_size, num_of_query_embeddings))
+            + " embeddings",
+        ):
+            # Do MS2DeepScore predictions for batch
+            end_idx = min(start_idx + batch_size, num_of_query_embeddings)
+            selected_query_embeddings = query_embeddings.embeddings[start_idx:end_idx]
+            score_matrix = cosine_similarity_matrix(selected_query_embeddings, self.reference_embeddings.embeddings)
+
+            highest_score_idx = np.argmax(score_matrix, axis=1)
+            highest_score_values = np.max(score_matrix, axis=1)
+
+            library_index_highest_ms2deepscore[start_idx:end_idx] = highest_score_idx
+            highest_ms2deepscore_values[start_idx:end_idx] = highest_score_values
+
+            # get predicted inchikeys
+            predicted_inchikeys = self.reference_metadata.iloc[highest_score_idx]["inchikey"]
+
+            # Compute MS2Query reliability score
+            ms2query_scores.extend(
+                get_ms2query_reliability_prediction(
+                    predicted_inchikeys,
+                    self.spectrum_indices_per_inchikey,
+                    self.top_k_tanimoto_scores,
+                    score_matrix,
+                )
+            )
+
+        # construct results df
+        results = self.reference_metadata.iloc[library_index_highest_ms2deepscore].copy()
+        results["predicted_tanimoto"] = highest_ms2deepscore_values
+        results["ms2query_reliability_prediction"] = ms2query_scores
+        return results
+
+
+def run_ms2query_from_files(
+    query_spectrum_file,
+    ms2deepscore_model_file_name,
+    reference_embeddings_file,
+    top_k_tanimoto_scores_file,
+    reference_metadata_file,
+    save_file_location,
+):
+    ms2query_library = ReferenceLibrary.load_from_files(
+        ms2deepscore_model_file_name,
+        reference_embeddings_file,
+        top_k_tanimoto_scores_file,
+        reference_metadata_file,
+    )
+
+    query_spectra = list(tqdm(load_spectra(query_spectrum_file), desc="loading_in_query_spectra"))
+    results_df = ms2query_library.run_ms2query(query_spectra)
+    results_df.to_csv(save_file_location)
+
+
+def get_ms2query_reliability_prediction(
+    predicted_inchikeys: list[str],
+    spectrum_indices_per_inchikey,
+    top_k_tanimoto_scores: TopKTanimotoScores,
+    ms2deepscore_score_matrix,
+) -> list[float]:
+    ms2query_scores = []
+    for query_spectrum_index, library_inchikey in enumerate(predicted_inchikeys):
+        top_k_inchikeys = top_k_tanimoto_scores.select_top_k_inchikeys(library_inchikey[:14])
+        maximum_ms2deepscores = np.zeros(top_k_tanimoto_scores.k, dtype=float)
+        for i, inchikey in enumerate(top_k_inchikeys):
+            spectrum_indexes = spectrum_indices_per_inchikey[inchikey]
+            highest_ms2deepscore = np.max(ms2deepscore_score_matrix[query_spectrum_index, spectrum_indexes])
+            maximum_ms2deepscores[i] = highest_ms2deepscore
+        ms2query_scores.append(np.mean(maximum_ms2deepscores))
+    # todo get the spectrum hashes instead of the indexes for lookup later.
+    return ms2query_scores
+
+
+def extract_metadata_from_library(spectra: AnnotatedSpectrumSet, metadata_to_collect: list):
+    collected_metadata = {key: [] for key in metadata_to_collect}
+    collected_metadata["spectrum_hashes"] = []
+    for spectrum in tqdm(spectra.spectra, desc="Extracting metadata df from spectra"):
+        for metadata_key in metadata_to_collect:
+            collected_metadata[metadata_key].append(spectrum.get(metadata_key))
+        collected_metadata["spectrum_hashes"].append(str(spectrum.__hash__()))
+    return pd.DataFrame(collected_metadata)
diff --git a/ms2query/benchmarking/TopKTanimotoScores.py b/ms2query/ms2query_development/TopKTanimotoScores.py
similarity index 69%
rename from ms2query/benchmarking/TopKTanimotoScores.py
rename to ms2query/ms2query_development/TopKTanimotoScores.py
index 07c0912..c930ecc 100644
--- a/ms2query/benchmarking/TopKTanimotoScores.py
+++ b/ms2query/ms2query_development/TopKTanimotoScores.py
@@ -1,7 +1,8 @@
+from pathlib import Path
 import numpy as np
 import pandas as pd
-from ms2query.benchmarking.Fingerprints import Fingerprints
 from ms2query.metrics import generalized_tanimoto_similarity_matrix
+from ms2query.ms2query_development.Fingerprints import Fingerprints
 
 
 class TopKTanimotoScores:
@@ -27,13 +28,21 @@ def _create_multi_index(
         combined_data = np.empty((len(inchikey_indexes), self.k * 2), dtype=object)
         combined_data[:, 0::2] = top_k_inchikeys
         combined_data[:, 1::2] = tanimoto_scores_for_top_k
-        return pd.DataFrame(combined_data, index=inchikey_indexes, columns=columns)
+        df = pd.DataFrame(combined_data, index=inchikey_indexes, columns=columns)
+
+        # Cast score columns to float64
+        score_cols = [(rank, "score") for rank in [f"Rank_{i + 1}" for i in range(self.k)]]
+        df[score_cols] = df[score_cols].astype(float)
+
+        return df
 
     @classmethod
     def calculate_from_fingerprints(cls, query_fingerprints: Fingerprints, target_fingerprints: Fingerprints, k):
         """
         Gets the top k highest inchikeys and scores for each inchikey in query_fingerprints from target_fingerprints
         """
+        if target_fingerprints.fingerprints.shape[0] < k:
+            raise ValueError("K cannot be larger than the number of fingerprints")
         similarity_scores = generalized_tanimoto_similarity_matrix(
             query_fingerprints.fingerprints, target_fingerprints.fingerprints
         )
@@ -67,3 +76,30 @@ def get_all_average_tanimoto_scores(self) -> dict[str, float]:
 
         average_per_inchikey_df = scores_df.mean(axis=1)
         return average_per_inchikey_df.to_dict()
+
+    def save(self, path: str | Path) -> None:
+        """Save the TopKTanimotoScores to disk as a parquet file.
+
+        Args:
+            path: File path without extension, e.g. "/data/top_k_scores".
+        """
+        Path(path).with_suffix(".parquet").parent.mkdir(parents=True, exist_ok=True)
+        self.top_k_inchikeys_and_scores.to_parquet(Path(path).with_suffix(".parquet"))
+
+    @classmethod
+    def load(cls, path: str | Path) -> "TopKTanimotoScores":
+        """Load a previously saved TopKTanimotoScores from disk.
+
+        Args:
+            path: File path without extension, e.g. "/data/top_k_scores".
+
+        Returns:
+            A fully reconstructed TopKTanimotoScores instance.
+        """
+        df = pd.read_parquet(Path(path).with_suffix(".parquet"))
+        df.columns.names = ["result_rank", "attribute"]
+
+        instance = cls.__new__(cls)
+        instance.k = len(df.columns.get_level_values("result_rank").unique())
+        instance.top_k_inchikeys_and_scores = df
+        return instance
diff --git a/ms2query/benchmarking/__init__.py b/ms2query/ms2query_development/__init__.py
similarity index 100%
rename from ms2query/benchmarking/__init__.py
rename to ms2query/ms2query_development/__init__.py
diff --git a/ms2query/benchmarking/reference_methods/EvaluateExactMatchSearch.py b/ms2query/ms2query_development/reference_methods/EvaluateExactMatchSearch.py
similarity index 98%
rename from ms2query/benchmarking/reference_methods/EvaluateExactMatchSearch.py
rename to ms2query/ms2query_development/reference_methods/EvaluateExactMatchSearch.py
index eced66b..a57368e 100644
--- a/ms2query/benchmarking/reference_methods/EvaluateExactMatchSearch.py
+++ b/ms2query/ms2query_development/reference_methods/EvaluateExactMatchSearch.py
@@ -1,7 +1,7 @@
 import random
 from typing import Callable, List, Tuple
 from tqdm import tqdm
-from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
 
 
 class EvaluateExactMatchSearchAcrossIonmodes:
diff --git a/ms2query/benchmarking/MS2DeepScoresForTopInChikeys.py b/ms2query/ms2query_development/reference_methods/MS2DeepScoresForTopInChikeys.py
similarity index 92%
rename from ms2query/benchmarking/MS2DeepScoresForTopInChikeys.py
rename to ms2query/ms2query_development/reference_methods/MS2DeepScoresForTopInChikeys.py
index d2519c7..883ab35 100644
--- a/ms2query/benchmarking/MS2DeepScoresForTopInChikeys.py
+++ b/ms2query/ms2query_development/reference_methods/MS2DeepScoresForTopInChikeys.py
@@ -1,12 +1,12 @@
 import numpy as np
 from ms2deepscore.vector_operations import cosine_similarity_matrix
 from tqdm import tqdm
-from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet
-from ms2query.benchmarking.Fingerprints import Fingerprints
-from ms2query.benchmarking.predict_top_k_ms2deepscore import (
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.Fingerprints import Fingerprints
+from ms2query.ms2query_development.reference_methods.predict_top_k_ms2deepscore import (
     select_inchikeys_with_highest_ms2deepscore,
 )
-from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores
+from ms2query.ms2query_development.TopKTanimotoScores import TopKTanimotoScores
 
 
 def calculate_MS2DeepScoresForTopKInChikeys_from_spectra(
@@ -77,6 +77,9 @@ def calculate_MS2DeepScoresForTopKInChikeys(
 class MS2DeepScoresForTopKInChikeys:
     """Stores the MS2DeepScores and Tanimoto scores for the top k closest lib spectra
 
+    This is only needed for the benchmarking and development (in the notebooks)
+    and is not used for running the final verison of MS2Query
+
     This allows for quick testing of different reranking strategies. E.g. get_mean is similar to the original MS2Query,
     but it can also be used to make matrixes with both MS2DeepScore and tanimoto scores to train small reranking models.
 
diff --git a/ms2query/benchmarking/reference_methods/__init__.py b/ms2query/ms2query_development/reference_methods/__init__.py
similarity index 100%
rename from ms2query/benchmarking/reference_methods/__init__.py
rename to ms2query/ms2query_development/reference_methods/__init__.py
diff --git a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py b/ms2query/ms2query_development/reference_methods/predict_best_possible_match.py
similarity index 93%
rename from ms2query/benchmarking/reference_methods/predict_best_possible_match.py
rename to ms2query/ms2query_development/reference_methods/predict_best_possible_match.py
index 1b82e04..e27d7ec 100644
--- a/ms2query/benchmarking/reference_methods/predict_best_possible_match.py
+++ b/ms2query/ms2query_development/reference_methods/predict_best_possible_match.py
@@ -1,7 +1,7 @@
 from typing import Dict
 from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix
-from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet
-from ms2query.benchmarking.Fingerprints import Fingerprints
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.Fingerprints import Fingerprints
 
 
 def predict_best_possible_match(
diff --git a/ms2query/benchmarking/predict_top_k_ms2deepscore.py b/ms2query/ms2query_development/reference_methods/predict_top_k_ms2deepscore.py
similarity index 96%
rename from ms2query/benchmarking/predict_top_k_ms2deepscore.py
rename to ms2query/ms2query_development/reference_methods/predict_top_k_ms2deepscore.py
index 1da3b2e..149807c 100644
--- a/ms2query/benchmarking/predict_top_k_ms2deepscore.py
+++ b/ms2query/ms2query_development/reference_methods/predict_top_k_ms2deepscore.py
@@ -2,8 +2,8 @@
 import numpy as np
 from ms2deepscore.vector_operations import cosine_similarity_matrix
 from tqdm import tqdm
-from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet
-from ms2query.benchmarking.Embeddings import Embeddings
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.Embeddings import Embeddings
 
 
 def predict_top_k_ms2deepscores(
diff --git a/ms2query/ms2query_development/reference_methods/readme.md b/ms2query/ms2query_development/reference_methods/readme.md
new file mode 100644
index 0000000..289ff6a
--- /dev/null
+++ b/ms2query/ms2query_development/reference_methods/readme.md
@@ -0,0 +1 @@
+All files here are not important to core functionality and in fact not used for running the final version of MS2Query. However MS2DeepSCoresForTopInChikeys and predict_top-k_ms2deepscore are core to the benchmarking and experimenting in the notebooks. predict_best_possible_match and EvaluateExactMatchSearch are not yet used for the benchmarking, but both should still be done, so this could be used for that. 
diff --git a/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb b/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb
index 4b244b2..80a3ba6 100644
--- a/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb
+++ b/ms2query/notebooks/develop_and_compare_ms2query_2.ipynb
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6fecef86-84af-40e9-bf48-d81d62f8bcf4",
    "metadata": {},
    "outputs": [
@@ -270,7 +270,7 @@
     }
    ],
    "source": [
-    "from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet\n",
+    "from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet\n",
     "\n",
     "pos_test_spectra = AnnotatedSpectrumSet.create_spectrum_set(pos_test_spectra)\n",
     "pos_train_spectra = AnnotatedSpectrumSet.create_spectrum_set(pos_train_spectra)\n",
@@ -279,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "92a0e90c-d435-4a0e-b9ba-bc7ab2139ca6",
    "metadata": {},
    "outputs": [
@@ -294,7 +294,7 @@
     }
    ],
    "source": [
-    "from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet\n",
+    "from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet\n",
     "\n",
     "neg_val_spectra = AnnotatedSpectrumSet.create_spectrum_set(neg_val_spectra)\n",
     "neg_test_spectra = AnnotatedSpectrumSet.create_spectrum_set(neg_test_spectra)\n",
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "3453717f-b50d-4623-bd87-b1287c0fe916",
    "metadata": {},
    "outputs": [
@@ -444,7 +444,7 @@
     }
    ],
    "source": [
-    "from ms2query.benchmarking.Fingerprints import Fingerprints\n",
+    "from ms2query.ms2query_development.Fingerprints import Fingerprints\n",
     "val_fingerprints = Fingerprints.from_spectrum_set(pos_val_spectra, \"daylight\", 4096)\n",
     "train_fingerprints = Fingerprints.from_spectrum_set(pos_train_spectra, \"daylight\", 4096)\n",
     "val_and_train_fingerprints = Fingerprints.combine_fingerprints(val_fingerprints, train_fingerprints)"
@@ -461,12 +461,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": null,
    "id": "cc395ecc-9473-4c9d-979f-ff8c087293d5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores\n",
+    "from ms2query.ms2query_development.TopKTanimotoScores import TopKTanimotoScores\n",
     "top_k_tanimoto_scores = TopKTanimotoScores.calculate_from_fingerprints(\n",
     "        train_fingerprints,\n",
     "        train_fingerprints, k=100,)"
@@ -488,7 +488,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ms2query.benchmarking.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n",
+    "from ms2query.ms2query_development.reference_methods.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n",
     "\n",
     "inchikeys_with_highest_ms2deepscores = select_inchikeys_with_highest_ms2deepscore(\n",
     "        pos_val_spectra, pos_train_spectra, nr_of_inchikeys_to_select=1, batch_size=1000,)\n"
@@ -509,7 +509,7 @@
     }
    ],
    "source": [
-    "from ms2query.benchmarking.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n",
+    "from ms2query.ms2query_development.reference_methods.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n",
     "close_tanimoto_scores = calculate_MS2DeepScoresForTopKInChikeys(pos_train_spectra, pos_val_spectra, top_k_tanimoto_scores, inchikeys_with_highest_ms2deepscores)"
    ]
   },
@@ -2630,10 +2630,10 @@
     }
    ],
    "source": [
-    "from ms2query.benchmarking.Fingerprints import Fingerprints\n",
-    "from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores\n",
-    "from ms2query.benchmarking.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n",
-    "from ms2query.benchmarking.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n",
+    "from ms2query.ms2query_development.Fingerprints import Fingerprints\n",
+    "from ms2query.ms2query_development.TopKTanimotoScores import TopKTanimotoScores\n",
+    "from ms2query.ms2query_development.reference_methods.predict_top_k_ms2deepscore import select_inchikeys_with_highest_ms2deepscore\n",
+    "from ms2query.ms2query_development.reference_methods.MS2DeepScoresForTopInChikeys import calculate_MS2DeepScoresForTopKInChikeys\n",
     "\n",
     "neg_val_fingerprints = Fingerprints.from_spectrum_set(neg_val_spectra, \"daylight\", 4096)\n",
     "neg_train_fingerprints = Fingerprints.from_spectrum_set(neg_train_spectra, \"daylight\", 4096)\n",
@@ -2714,7 +2714,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ms2query.benchmarking.predict_top_k_ms2deepscore import predict_top_k_ms2deepscores\n",
+    "from ms2query.ms2query_development.reference_methods.predict_top_k_ms2deepscore import predict_top_k_ms2deepscores\n",
     "\n",
     "indexes, ms2deepscores = predict_top_k_ms2deepscores(neg_train_spectra.embeddings, neg_val_spectra.embeddings)\n",
     "neg_predicted_inchikeys_ms2deepscore = [neg_train_spectra.spectra[int(index)].get(\"inchikey\")[:14] for index in indexes]\n",
diff --git a/ms2query/notebooks/tutorial.ipynb b/ms2query/notebooks/tutorial.ipynb
new file mode 100644
index 0000000..7afc892
--- /dev/null
+++ b/ms2query/notebooks/tutorial.ipynb
@@ -0,0 +1,1109 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "70e60490",
+   "metadata": {},
+   "source": [
+    "# Tutorial for running MS2Query and creating your own libraries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fdc846e",
+   "metadata": {},
+   "source": [
+    "### Download already created library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "5ce3469d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "def download_file(link, file_name):\n",
+    "    response = requests.get(link, stream=True)\n",
+    "    if os.path.exists(file_name):\n",
+    "        print(f\"The file {file_name} already exists, the file won't be downloaded\")\n",
+    "        return\n",
+    "    total_size = int(response.headers.get('content-length', 0))\n",
+    "\n",
+    "    with open(file_name, \"wb\") as f, tqdm(desc=\"Downloading file\", total=total_size, unit='B', unit_scale=True, unit_divisor=1024,) as bar:\n",
+    "        for chunk in response.iter_content(chunk_size=1024):\n",
+    "            if chunk:\n",
+    "                f.write(chunk)\n",
+    "                bar.update(len(chunk))  # Update progress bar by the chunk size\n",
+    "folder_to_store_zenodo_files = \"./zenodo_files/full_library\"\n",
+    "os.makedirs(folder_to_store_zenodo_files, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "125332e2-fb26-45a4-b649-b84a7ed88181",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file ./zenodo_files/full_library/embeddings.npz already exists, the file won't be downloaded\n",
+      "The file ./zenodo_files/full_library/library_metadata.parquet already exists, the file won't be downloaded\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file ./zenodo_files/full_library/top_k_tanimoto_scores.parquet already exists, the file won't be downloaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "download_file(\"https://zenodo.org/records/19206616/files/embeddings.npz?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"embeddings.npz\"))\n",
+    "download_file(\"https://zenodo.org/records/19206616/files/library_metadata.parquet?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"library_metadata.parquet\"))\n",
+    "download_file(\"https://zenodo.org/records/19206616/files/ms2deepscore_model?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))\n",
+    "download_file(\"https://zenodo.org/records/19206616/files/top_k_tanimoto_scores.parquet?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"top_k_tanimoto_scores.parquet\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa436a42-d850-4ce2-86c5-741646c8b168",
+   "metadata": {},
+   "source": [
+    "# initialize reference library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "8b477da9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ms2query.ms2query_development.ReferenceLibrary import ReferenceLibrary\n",
+    "from pathlib import Path\n",
+    "reference_library = ReferenceLibrary.load_from_files(os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"),\n",
+    "                                                    os.path.join(folder_to_store_zenodo_files, \"embeddings.npz\"),\n",
+    "                                                    os.path.join(folder_to_store_zenodo_files, \"top_k_tanimoto_scores.parquet\"),\n",
+    "                                                    os.path.join(folder_to_store_zenodo_files, \"library_metadata.parquet\"))\n",
+    "\n",
+    "# Or directly find the files from a directory\n",
+    "# reference_library = ReferenceLibrary.load_from_directory(Path(folder_to_store_zenodo_files))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b637cbac-c79b-4df3-8ad9-0db400a32fb4",
+   "metadata": {},
+   "source": [
+    "# Run MS2Query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "ae96dd88-c8c0-4be0-9355-b29a8f5ce37b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "10it [00:00, 83.01it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_spectra = list(tqdm(load_from_mgf(\"../../tests/test_data/10_spectra.mgf\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "ec952eaa-102f-4d86-8fbb-b91973f395a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Hashing spe\n",
+      "Computing s\n",
+      "Predicting \n",
+      "/lustre/BIF/nobackup/jonge094/ms2query_2/ms2query/ms2query_development/ReferenceLibrary.py:168: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  # construct results df\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>precursor_mz</th>\n",
+       "      <th>retention_time</th>\n",
+       "      <th>collision_energy</th>\n",
+       "      <th>compound_name</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>inchikey</th>\n",
+       "      <th>spectrum_hashes</th>\n",
+       "      <th>ms2query_reliability_prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>29144</th>\n",
+       "      <td>181.06100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Nicotinuric acid</td>\n",
+       "      <td>O=C(O)CNC(=O)c1cccnc1</td>\n",
+       "      <td>ZBSGKPYXQINNGF-UHFFFAOYSA-N</td>\n",
+       "      <td>4538586676799711060475687385406365205312522933...</td>\n",
+       "      <td>0.524193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29138</th>\n",
+       "      <td>181.06100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Nicotinuric acid</td>\n",
+       "      <td>O=C(O)CNC(=O)c1cccnc1</td>\n",
+       "      <td>ZBSGKPYXQINNGF-UHFFFAOYSA-N</td>\n",
+       "      <td>4691175720354550216218758199469384349519880571...</td>\n",
+       "      <td>0.477547</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29139</th>\n",
+       "      <td>181.06100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Nicotinuric acid</td>\n",
+       "      <td>O=C(O)CNC(=O)c1cccnc1</td>\n",
+       "      <td>ZBSGKPYXQINNGF-UHFFFAOYSA-N</td>\n",
+       "      <td>8126043578764904179452276022324867195623210187...</td>\n",
+       "      <td>0.525872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23334</th>\n",
+       "      <td>181.00700</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>thieno[2,3-d]pyrimidine-4-carboxylic acid</td>\n",
+       "      <td>O=C(O)c1ncnc2sccc12</td>\n",
+       "      <td>VOXKHPSKDOLCAC-UHFFFAOYSA-N</td>\n",
+       "      <td>4621371819674604814601843392349447447010638904...</td>\n",
+       "      <td>0.392072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29146</th>\n",
+       "      <td>181.06100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Nicotinuric acid</td>\n",
+       "      <td>O=C(O)CNC(=O)c1cccnc1</td>\n",
+       "      <td>ZBSGKPYXQINNGF-UHFFFAOYSA-N</td>\n",
+       "      <td>4605782118791699534560809552554836164365415626...</td>\n",
+       "      <td>0.511462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478528</th>\n",
+       "      <td>202.04300</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>thiabendazole</td>\n",
+       "      <td>c1ccc2[nH]c(-c3cscn3)nc2c1</td>\n",
+       "      <td>WJCNZQLZVWNLKY-UHFFFAOYSA-N</td>\n",
+       "      <td>4037303697924680981028453225819741544585843750...</td>\n",
+       "      <td>0.487617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209484</th>\n",
+       "      <td>323.13902</td>\n",
+       "      <td>105.64</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>5-[(2,5-dimethylphenoxy)methyl]-N-(4-pyridinyl...</td>\n",
+       "      <td>Cc1ccc(C)c(OCc2ccc(C(=O)Nc3ccncc3)o2)c1</td>\n",
+       "      <td>VYTRUHMGFSNSKK-UHFFFAOYSA-N</td>\n",
+       "      <td>4704480111668309422204405940015522296005033315...</td>\n",
+       "      <td>0.396629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478527</th>\n",
+       "      <td>202.04300</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>thiabendazole</td>\n",
+       "      <td>c1ccc2[nH]c(-c3cscn3)nc2c1</td>\n",
+       "      <td>WJCNZQLZVWNLKY-UHFFFAOYSA-N</td>\n",
+       "      <td>4354824398973859393735759007513142956866549634...</td>\n",
+       "      <td>0.472113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>912123</th>\n",
+       "      <td>330.18200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>N-[(1R,2S)-2-(4-ethoxyphenyl)cyclopropyl]-3,4-...</td>\n",
+       "      <td>CCOc1ccc(C2CC2NC(=O)N2CC(=O)N(C)C(C)C2)cc1</td>\n",
+       "      <td>FUDGTDMCXMAZHS-UHFFFAOYSA-N</td>\n",
+       "      <td>4689876070452517240544538176971004829216063873...</td>\n",
+       "      <td>0.487231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>478527</th>\n",
+       "      <td>202.04300</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>thiabendazole</td>\n",
+       "      <td>c1ccc2[nH]c(-c3cscn3)nc2c1</td>\n",
+       "      <td>WJCNZQLZVWNLKY-UHFFFAOYSA-N</td>\n",
+       "      <td>4354824398973859393735759007513142956866549634...</td>\n",
+       "      <td>0.490491</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        precursor_mz  retention_time collision_energy  \\\n",
+       "29144      181.06100             NaN             None   \n",
+       "29138      181.06100             NaN             None   \n",
+       "29139      181.06100             NaN             None   \n",
+       "23334      181.00700             NaN             None   \n",
+       "29146      181.06100             NaN             None   \n",
+       "478528     202.04300             NaN             None   \n",
+       "209484     323.13902          105.64             20.0   \n",
+       "478527     202.04300             NaN             None   \n",
+       "912123     330.18200             NaN             None   \n",
+       "478527     202.04300             NaN             None   \n",
+       "\n",
+       "                                            compound_name  \\\n",
+       "29144                                    Nicotinuric acid   \n",
+       "29138                                    Nicotinuric acid   \n",
+       "29139                                    Nicotinuric acid   \n",
+       "23334           thieno[2,3-d]pyrimidine-4-carboxylic acid   \n",
+       "29146                                    Nicotinuric acid   \n",
+       "478528                                      thiabendazole   \n",
+       "209484  5-[(2,5-dimethylphenoxy)methyl]-N-(4-pyridinyl...   \n",
+       "478527                                      thiabendazole   \n",
+       "912123  N-[(1R,2S)-2-(4-ethoxyphenyl)cyclopropyl]-3,4-...   \n",
+       "478527                                      thiabendazole   \n",
+       "\n",
+       "                                            smiles  \\\n",
+       "29144                        O=C(O)CNC(=O)c1cccnc1   \n",
+       "29138                        O=C(O)CNC(=O)c1cccnc1   \n",
+       "29139                        O=C(O)CNC(=O)c1cccnc1   \n",
+       "23334                          O=C(O)c1ncnc2sccc12   \n",
+       "29146                        O=C(O)CNC(=O)c1cccnc1   \n",
+       "478528                  c1ccc2[nH]c(-c3cscn3)nc2c1   \n",
+       "209484     Cc1ccc(C)c(OCc2ccc(C(=O)Nc3ccncc3)o2)c1   \n",
+       "478527                  c1ccc2[nH]c(-c3cscn3)nc2c1   \n",
+       "912123  CCOc1ccc(C2CC2NC(=O)N2CC(=O)N(C)C(C)C2)cc1   \n",
+       "478527                  c1ccc2[nH]c(-c3cscn3)nc2c1   \n",
+       "\n",
+       "                           inchikey  \\\n",
+       "29144   ZBSGKPYXQINNGF-UHFFFAOYSA-N   \n",
+       "29138   ZBSGKPYXQINNGF-UHFFFAOYSA-N   \n",
+       "29139   ZBSGKPYXQINNGF-UHFFFAOYSA-N   \n",
+       "23334   VOXKHPSKDOLCAC-UHFFFAOYSA-N   \n",
+       "29146   ZBSGKPYXQINNGF-UHFFFAOYSA-N   \n",
+       "478528  WJCNZQLZVWNLKY-UHFFFAOYSA-N   \n",
+       "209484  VYTRUHMGFSNSKK-UHFFFAOYSA-N   \n",
+       "478527  WJCNZQLZVWNLKY-UHFFFAOYSA-N   \n",
+       "912123  FUDGTDMCXMAZHS-UHFFFAOYSA-N   \n",
+       "478527  WJCNZQLZVWNLKY-UHFFFAOYSA-N   \n",
+       "\n",
+       "                                          spectrum_hashes  \\\n",
+       "29144   4538586676799711060475687385406365205312522933...   \n",
+       "29138   4691175720354550216218758199469384349519880571...   \n",
+       "29139   8126043578764904179452276022324867195623210187...   \n",
+       "23334   4621371819674604814601843392349447447010638904...   \n",
+       "29146   4605782118791699534560809552554836164365415626...   \n",
+       "478528  4037303697924680981028453225819741544585843750...   \n",
+       "209484  4704480111668309422204405940015522296005033315...   \n",
+       "478527  4354824398973859393735759007513142956866549634...   \n",
+       "912123  4689876070452517240544538176971004829216063873...   \n",
+       "478527  4354824398973859393735759007513142956866549634...   \n",
+       "\n",
+       "        ms2query_reliability_prediction  \n",
+       "29144                          0.524193  \n",
+       "29138                          0.477547  \n",
+       "29139                          0.525872  \n",
+       "23334                          0.392072  \n",
+       "29146                          0.511462  \n",
+       "478528                         0.487617  \n",
+       "209484                         0.396629  \n",
+       "478527                         0.472113  \n",
+       "912123                         0.487231  \n",
+       "478527                         0.490491  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reference_library.run_ms2query(test_spectra)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc3f2477",
+   "metadata": {},
+   "source": [
+    "# Create a new MS2Query reference library\n",
+    "Here we show how to create the reference library that can be downloaded from Zenodo. But if you want to make your own of course replace with your own files (no need to download the library)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2501dc1e-9ab0-408d-b869-8cd9874a46d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_file(\"https://zenodo.org/records/16882111/files/merged_and_cleaned_libraries_1.mgf?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))\n",
+    "download_file(\"https://zenodo.org/records/17826815/files/ms2deepscore_model.pt?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cae72056-c3d3-4f4f-b976-c47c86002007",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "library_spectra = list(tqdm(load_from_mgf(os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09c62f09-ac8e-4b06-a286-28b18bf61af8",
+   "metadata": {},
+   "source": [
+    "### Creates the library and saves all the files for future usage. (the files you can also just download above)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8067f86d-9120-4611-bae0-5a70159c4328",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Create mapping from inchikey to spectrum: 100%|█████████████████████████████████████████████████████████████████████████████████| 1017531/1017531 [00:05<00:00, 183656.80it/s]\n",
+      "Hashing spectra: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1017531/1017531 [01:27<00:00, 11680.81it/s]\n",
+      "Computing spectral embeddings ...: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 1017531/1017531 [40:32<00:00, 418.26it/s]\n",
+      "Get most common inchi per inchikey: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 67648/67648 [00:03<00:00, 17833.31it/s]\n",
+      "Adding fingerprints to Inchikeys: 67648it [10:40, 105.60it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "reference_library = ReferenceLibrary.create_from_spectra(library_spectra, os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"), store_file_directory = Path(os.path.join(folder_to_store_zenodo_files, \"full_library\")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd223f01-e70d-4a0c-8e68-2f28d7746a55",
+   "metadata": {},
+   "source": [
+    "# Sanity check that benchmarking resuls also work with new library creation\n",
+    "Not part of the tutorial, just internal documentation. We test the negative in negative library searching. To check that it matches the original benmarking notebook. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b7df418e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file ./zenodo_files/data_split_inchikeys.json already exists, the file won't be downloaded\n",
+      "The file ./zenodo_files/merged_and_cleaned_libraries_1.mgf already exists, the file won't be downloaded\n",
+      "The file ./zenodo_files/ms2deepscore_model.pt already exists, the file won't be downloaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.makedirs(folder_to_store_zenodo_files, exist_ok=True)\n",
+    "# This file contains the inchikeys used for the data split. This ensures reproducing the exact same results, \n",
+    "# alternatively you can of course do the data split yourself, but results might differ slightly. \n",
+    "download_file(\"https://zenodo.org/records/18801895/files/data_split_inchikeys.json?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"data_split_inchikeys.json\"))\n",
+    "download_file(\"https://zenodo.org/records/16882111/files/merged_and_cleaned_libraries_1.mgf?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))\n",
+    "download_file(\"https://zenodo.org/records/17826815/files/ms2deepscore_model.pt?download=1\", \n",
+    "              os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f521c1c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "730577it [04:27, 2631.43it/s]"
+     ]
+    }
+   ],
+   "source": [
+    "from matchms.importing import load_from_mgf\n",
+    "from tqdm import tqdm\n",
+    "import os\n",
+    "library_spectra = list(tqdm(load_from_mgf(os.path.join(folder_to_store_zenodo_files, \"merged_and_cleaned_libraries_1.mgf\"))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25be7410",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "with open(os.path.join(folder_to_store_zenodo_files, \"data_split_inchikeys.json\"), \"r\") as f:\n",
+    "    data_split = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27a82f53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_inchikeys = set(data_split[\"val\"])\n",
+    "train_inchikeys = set(data_split[\"train\"])\n",
+    "test_inchikeys = set(data_split[\"test\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf5b0447",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_spectra = []\n",
+    "test_spectra = []\n",
+    "train_spectra = []\n",
+    "for spectrum in tqdm(library_spectra):\n",
+    "    inchikey = spectrum.get(\"inchikey\")[:14]\n",
+    "    if inchikey in train_inchikeys:\n",
+    "        train_spectra.append(spectrum)\n",
+    "    elif inchikey in val_inchikeys:\n",
+    "        val_spectra.append(spectrum)\n",
+    "    elif inchikey in test_inchikeys:\n",
+    "        test_spectra.append(spectrum)\n",
+    "    else:\n",
+    "        raise ValueError(f\"The inchikey {inchikey} does not appear in the val, test or train inchikeys\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "76471c2c-d7ab-4058-a42b-31b975e250ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_on_ionmode(spectra):\n",
+    "    pos_spectra = []\n",
+    "    neg_spectra = []\n",
+    "    for spectrum in spectra:\n",
+    "        ionmode = spectrum.get(\"ionmode\")\n",
+    "        if ionmode == \"positive\":\n",
+    "            pos_spectra.append(spectrum)\n",
+    "        elif ionmode == \"negative\":\n",
+    "            neg_spectra.append(spectrum)\n",
+    "        else:\n",
+    "            raise ValueError(f\"The ionmode {ionmode} is not positive or negative\")\n",
+    "    return pos_spectra, neg_spectra"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "02be88d5-3962-4c8e-89ab-72fa7163d47c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pos_val_spectra, neg_val_spectra = split_on_ionmode(val_spectra)\n",
+    "pos_test_spectra, neg_test_spectra = split_on_ionmode(test_spectra)\n",
+    "pos_train_spectra, neg_train_spectra = split_on_ionmode(train_spectra)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "669a489a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Create mapping from inchikey to spectrum: 100%|███████████████████████████████████████████████████████████████████████████████████| 283790/283790 [00:01<00:00, 282229.91it/s]\n",
+      "Hashing spectra: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 283790/283790 [00:19<00:00, 14677.55it/s]\n",
+      "Computing spectral embeddings ...:  11%|██████████                                                                                    | 30305/283790 [00:31<04:24, 958.90it/s]"
+     ]
+    }
+   ],
+   "source": [
+    "from ms2query.ms2query_development.ReferenceLibrary import ReferenceLibrary\n",
+    "reference_library = ReferenceLibrary.create_from_spectra(neg_train_spectra, os.path.join(folder_to_store_zenodo_files, \"ms2deepscore_model.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "829714f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = reference_library.run_ms2query(neg_val_spectra)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c9f79c50-0c19-4a9c-afd1-115233581193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.to_csv(os.path.join(folder_to_store_zenodo_files, \"neg_neg_results.csv\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "49771619-f9ed-46a1-8b9f-5ae70377fd7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>precursor_mz</th>\n",
+       "      <th>retention_time</th>\n",
+       "      <th>collision_energy</th>\n",
+       "      <th>compound_name</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>inchikey</th>\n",
+       "      <th>spectrum_hashes</th>\n",
+       "      <th>ms2query_reliability_prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4574</th>\n",
+       "      <td>266.05894</td>\n",
+       "      <td>69.35</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>379724-54-6</td>\n",
+       "      <td>O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1</td>\n",
+       "      <td>FGLQAKJRFPNIJT-UHFFFAOYSA-N</td>\n",
+       "      <td>4287947218867478945721777143654811527279348390...</td>\n",
+       "      <td>0.739373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4576</th>\n",
+       "      <td>266.05894</td>\n",
+       "      <td>69.35</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>379724-54-6</td>\n",
+       "      <td>O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1</td>\n",
+       "      <td>FGLQAKJRFPNIJT-UHFFFAOYSA-N</td>\n",
+       "      <td>8445518326529625949101509334596600018097006285...</td>\n",
+       "      <td>0.605217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4574</th>\n",
+       "      <td>266.05894</td>\n",
+       "      <td>69.35</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>379724-54-6</td>\n",
+       "      <td>O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1</td>\n",
+       "      <td>FGLQAKJRFPNIJT-UHFFFAOYSA-N</td>\n",
+       "      <td>4287947218867478945721777143654811527279348390...</td>\n",
+       "      <td>0.739373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4578</th>\n",
+       "      <td>266.05900</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>379724-54-6</td>\n",
+       "      <td>O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1</td>\n",
+       "      <td>FGLQAKJRFPNIJT-UHFFFAOYSA-N</td>\n",
+       "      <td>4453904535900060540047468210876322356832506165...</td>\n",
+       "      <td>0.759073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4581</th>\n",
+       "      <td>266.05900</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>379724-54-6</td>\n",
+       "      <td>O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1</td>\n",
+       "      <td>FGLQAKJRFPNIJT-UHFFFAOYSA-N</td>\n",
+       "      <td>4438257318628859128622642248978796499540902384...</td>\n",
+       "      <td>0.770879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283597</th>\n",
+       "      <td>740.52030</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>PE 36:3</td>\n",
+       "      <td>CCCCCC=CCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCC=CCC...</td>\n",
+       "      <td>GKAFCSRKMWFPSJ-UHFFFAOYSA-N</td>\n",
+       "      <td>4605131291822750236269081691830926191534825838...</td>\n",
+       "      <td>0.943663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283597</th>\n",
+       "      <td>740.52030</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>PE 36:3</td>\n",
+       "      <td>CCCCCC=CCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCC=CCC...</td>\n",
+       "      <td>GKAFCSRKMWFPSJ-UHFFFAOYSA-N</td>\n",
+       "      <td>4605131291822750236269081691830926191534825838...</td>\n",
+       "      <td>0.943663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201773</th>\n",
+       "      <td>744.55300</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>PE(18:0_18:1) - (2-aminoethoxy)[2-[octadec-9-e...</td>\n",
+       "      <td>CCCCCCCCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCCCCCCC...</td>\n",
+       "      <td>JQKOHRZNEOQNJE-UHFFFAOYSA-N</td>\n",
+       "      <td>8543555785430847640307667357763008254598100629...</td>\n",
+       "      <td>0.921203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235132</th>\n",
+       "      <td>766.54100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1-Octadecanoyl-2-(5Z,8Z,11Z,14Z-eicosatetraeno...</td>\n",
+       "      <td>CCCCCC=CCC=CCC=CCC=CCCCC(=O)OC(COC(=O)CCCCCCCC...</td>\n",
+       "      <td>ANRKEHNWXKCXDB-UHFFFAOYSA-N</td>\n",
+       "      <td>4772335068029728878564587931928277302571040079...</td>\n",
+       "      <td>0.907530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235132</th>\n",
+       "      <td>766.54100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>1-Octadecanoyl-2-(5Z,8Z,11Z,14Z-eicosatetraeno...</td>\n",
+       "      <td>CCCCCC=CCC=CCC=CCC=CCCCC(=O)OC(COC(=O)CCCCCCCC...</td>\n",
+       "      <td>ANRKEHNWXKCXDB-UHFFFAOYSA-N</td>\n",
+       "      <td>4772335068029728878564587931928277302571040079...</td>\n",
+       "      <td>0.907530</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15967 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        precursor_mz  retention_time collision_energy  \\\n",
+       "4574       266.05894           69.35             30.0   \n",
+       "4576       266.05894           69.35             60.0   \n",
+       "4574       266.05894           69.35             30.0   \n",
+       "4578       266.05900             NaN             None   \n",
+       "4581       266.05900             NaN             None   \n",
+       "...              ...             ...              ...   \n",
+       "283597     740.52030             NaN             45.0   \n",
+       "283597     740.52030             NaN             45.0   \n",
+       "201773     744.55300             NaN             20.0   \n",
+       "235132     766.54100             NaN             30.0   \n",
+       "235132     766.54100             NaN             30.0   \n",
+       "\n",
+       "                                            compound_name  \\\n",
+       "4574                                          379724-54-6   \n",
+       "4576                                          379724-54-6   \n",
+       "4574                                          379724-54-6   \n",
+       "4578                                          379724-54-6   \n",
+       "4581                                          379724-54-6   \n",
+       "...                                                   ...   \n",
+       "283597                                            PE 36:3   \n",
+       "283597                                            PE 36:3   \n",
+       "201773  PE(18:0_18:1) - (2-aminoethoxy)[2-[octadec-9-e...   \n",
+       "235132  1-Octadecanoyl-2-(5Z,8Z,11Z,14Z-eicosatetraeno...   \n",
+       "235132  1-Octadecanoyl-2-(5Z,8Z,11Z,14Z-eicosatetraeno...   \n",
+       "\n",
+       "                                                   smiles  \\\n",
+       "4574                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "4576                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "4574                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "4578                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "4581                    O=C(O)C1CCN(C(=O)c2ccc(Cl)cc2)CC1   \n",
+       "...                                                   ...   \n",
+       "283597  CCCCCC=CCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCC=CCC...   \n",
+       "283597  CCCCCC=CCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCC=CCC...   \n",
+       "201773  CCCCCCCCC=CCCCCCCCC(=O)OC(COC(=O)CCCCCCCCCCCCC...   \n",
+       "235132  CCCCCC=CCC=CCC=CCC=CCCCC(=O)OC(COC(=O)CCCCCCCC...   \n",
+       "235132  CCCCCC=CCC=CCC=CCC=CCCCC(=O)OC(COC(=O)CCCCCCCC...   \n",
+       "\n",
+       "                           inchikey  \\\n",
+       "4574    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "4576    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "4574    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "4578    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "4581    FGLQAKJRFPNIJT-UHFFFAOYSA-N   \n",
+       "...                             ...   \n",
+       "283597  GKAFCSRKMWFPSJ-UHFFFAOYSA-N   \n",
+       "283597  GKAFCSRKMWFPSJ-UHFFFAOYSA-N   \n",
+       "201773  JQKOHRZNEOQNJE-UHFFFAOYSA-N   \n",
+       "235132  ANRKEHNWXKCXDB-UHFFFAOYSA-N   \n",
+       "235132  ANRKEHNWXKCXDB-UHFFFAOYSA-N   \n",
+       "\n",
+       "                                          spectrum_hashes  \\\n",
+       "4574    4287947218867478945721777143654811527279348390...   \n",
+       "4576    8445518326529625949101509334596600018097006285...   \n",
+       "4574    4287947218867478945721777143654811527279348390...   \n",
+       "4578    4453904535900060540047468210876322356832506165...   \n",
+       "4581    4438257318628859128622642248978796499540902384...   \n",
+       "...                                                   ...   \n",
+       "283597  4605131291822750236269081691830926191534825838...   \n",
+       "283597  4605131291822750236269081691830926191534825838...   \n",
+       "201773  8543555785430847640307667357763008254598100629...   \n",
+       "235132  4772335068029728878564587931928277302571040079...   \n",
+       "235132  4772335068029728878564587931928277302571040079...   \n",
+       "\n",
+       "        ms2query_reliability_prediction  \n",
+       "4574                           0.739373  \n",
+       "4576                           0.605217  \n",
+       "4574                           0.739373  \n",
+       "4578                           0.759073  \n",
+       "4581                           0.770879  \n",
+       "...                                 ...  \n",
+       "283597                         0.943663  \n",
+       "283597                         0.943663  \n",
+       "201773                         0.921203  \n",
+       "235132                         0.907530  \n",
+       "235132                         0.907530  \n",
+       "\n",
+       "[15967 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "6d9d10f8-a042-48c2-a9d1-b925815b9ac0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_inchikeys = [inchikey[:14] for inchikey in results[\"inchikey\"].tolist()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "de56ab2f-1e81-462a-9197-7d6099c22ad3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_scores = results[\"ms2query_reliability_prediction\"].tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b4bd52eb-742e-4a0b-bdaf-77b7a182a4e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import os\n",
+    "intermediates_folder = \"./cross_ionmode_intermediate\"\n",
+    "\n",
+    "with open(os.path.join(intermediates_folder, \"all_fingerprints.pickle\"), \"rb\") as handle:\n",
+    "    all_fingerprints = pickle.load(handle)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "1905a382-d0d5-4c45-a2c6-11358e509e70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "from matchms.similarity.vector_similarity_functions import jaccard_similarity_matrix\n",
+    "import numpy as np\n",
+    "def get_prediction_tanimoto(predicted_inchikeys, val_spectra, val_and_train_fingerprints):\n",
+    "    assert len(predicted_inchikeys) == len(val_spectra)\n",
+    "\n",
+    "    tanimoto_for_prediction = []\n",
+    "    for index, predicted_inchikey in enumerate(tqdm(predicted_inchikeys)):\n",
+    "        if predicted_inchikey is not None:\n",
+    "            correct_inchikey = val_spectra[index].get(\"inchikey\")[:14]\n",
+    "            predicted_fingerprint = val_and_train_fingerprints.get_fingerprints([predicted_inchikey])[0]\n",
+    "            actual_fingerprint = val_and_train_fingerprints.get_fingerprints([correct_inchikey])[0]\n",
+    "            tanimoto_prediction = jaccard_similarity_matrix(np.array([predicted_fingerprint]), np.array([actual_fingerprint]))[0][0]\n",
+    "            tanimoto_for_prediction.append(tanimoto_prediction)\n",
+    "        else:\n",
+    "            tanimoto_for_prediction.append(0.0)\n",
+    "    return tanimoto_for_prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "1ec0c831-ba10-4c0f-907e-6b7420f35ebd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████| 15967/15967 [00:00<00:00, 18131.11it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "neg_neg_tanimoto_for_prediction = get_prediction_tanimoto(predicted_inchikeys, neg_val_spectra, all_fingerprints)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "7eeccfdc-1542-43fc-ba7c-79ece4346a4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def create_values_violin_plot(predicted_scores, val_spectra, tanimoto_for_predictions, n_buckets):\n",
+    "    \"\"\"The predictions are ranked based on the predicted scores, and are split in different groups from high to low, \n",
+    "    for each group the average tanimoto per inchikey is computed.\"\"\"\n",
+    "    tanimoto_per_bucket = []\n",
+    "    ranked_indexes_per_bucket = bucket_by_value(predicted_scores, n_buckets)\n",
+    "    for indexes_in_bucket in ranked_indexes_per_bucket: \n",
+    "        average_tanimoto_per_inchikey = get_average_tanimoto_per_inchikey(val_spectra, tanimoto_for_predictions, indexes_in_bucket)\n",
+    "        tanimoto_per_bucket.append(average_tanimoto_per_inchikey)\n",
+    "    return tanimoto_per_bucket\n",
+    "\n",
+    "def bucket_by_value(values, n_buckets=10):\n",
+    "    \"\"\"\n",
+    "    Splits values into n equally sized buckets based on rank. \n",
+    "    So the highest scores are in the first bucket en the lowest in the last. Each bucket has the same number of values. \n",
+    "    \"\"\"\n",
+    "    values = np.array(values)\n",
+    "    # Get indexes that would sort the array descending (top values first)\n",
+    "    sorted_indexes = np.argsort(values)[::-1]\n",
+    "    \n",
+    "    # Split sorted indexes into n equal(ish) buckets\n",
+    "    buckets = np.array_split(sorted_indexes, n_buckets)\n",
+    "    \n",
+    "    return buckets\n",
+    "    \n",
+    "def get_average_tanimoto_per_inchikey(query_spectrum_set, tanimoto_for_predictions, selected_indexes):\n",
+    "    \"\"\"Calculates the average tanimoto per inchikey for the selected indexes\"\"\"\n",
+    "    assert len(tanimoto_for_predictions) == len(query_spectrum_set)\n",
+    "    average_scores_per_inchikey = []\n",
+    "    # Calculate score per unique inchikey\n",
+    "    for inchikey in query_spectrum_set.spectrum_indices_per_inchikey.keys():\n",
+    "        matching_spectrum_indexes = query_spectrum_set.spectrum_indices_per_inchikey[inchikey]\n",
+    "        prediction_scores = []\n",
+    "        for index in matching_spectrum_indexes:\n",
+    "            if index in selected_indexes:\n",
+    "                prediction_scores.append(tanimoto_for_predictions[index])\n",
+    "        if len(prediction_scores) > 0:\n",
+    "            average_prediction = sum(prediction_scores) / len(prediction_scores)\n",
+    "            average_scores_per_inchikey.append(average_prediction)\n",
+    "    return average_scores_per_inchikey\n",
+    "\n",
+    "def plot_average_per_threshold(predicted_scores, tanimoto_for_predictions, val_spectra, n_of_buckets):\n",
+    "    buckets = bucket_by_value(predicted_scores,n_of_buckets)\n",
+    "    mean_per_fraction = []\n",
+    "    selected_indexes = set()\n",
+    "    for indexes_in_bucket in tqdm(buckets):\n",
+    "        selected_indexes.update(set(indexes_in_bucket.tolist()))\n",
+    "        average_scores_per_inchikey = get_average_tanimoto_per_inchikey(val_spectra, tanimoto_for_predictions, selected_indexes)\n",
+    "        mean = sum(average_scores_per_inchikey)/len(average_scores_per_inchikey)\n",
+    "        mean_per_fraction.append(mean)\n",
+    "    return mean_per_fraction\n",
+    "    \n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "def plot_comparison_plot(results):\n",
+    "    n = len(list(results.values())[0])\n",
+    "    labels = [f\"0 - {(100/n)*(i+1)} %\" for i in range(n)]\n",
+    "    for label, mean_per_fraction in results.items():\n",
+    "        plt.plot(labels, mean_per_fraction, label = label)\n",
+    "    \n",
+    "    plt.xlabel(\"Fraction predicted\")\n",
+    "    plt.ylabel(\"Average Tanimoto\")\n",
+    "    plt.legend()\n",
+    "    plt.ylim((0, 1))\n",
+    "    \n",
+    "    step = n // 10\n",
+    "    positions = range(len(labels))\n",
+    "    \n",
+    "    plt.xticks(\n",
+    "        ticks=positions[step-1::step],    # start at 9, then 19, 29, ...\n",
+    "        labels=labels[step-1::step],\n",
+    "        rotation=45,\n",
+    "        ha='right'\n",
+    "    )\n",
+    "    plt.grid(axis='both', linestyle='--', alpha=0.5)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "3bf6e470-aa36-4c07-85d0-474d0e52002b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Create mapping from inchikey to spectrum: 100%|████████████████████████████████████| 15967/15967 [00:00<00:00, 367569.63it/s]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 603.54it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "plot_comparison_plot({\n",
+    "    # \"MS2DeepScore\":  plot_average_per_threshold(ms2deepscores.squeeze(), neg_neg_tanimoto_for_prediction_ms2deepscore, neg_val_spectra, 100),\n",
+    "    \"MS2Query 2.0\": plot_average_per_threshold(predicted_scores, neg_neg_tanimoto_for_prediction, AnnotatedSpectrumSet.create_spectrum_set(neg_val_spectra), 100),\n",
+    "})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ms2query/readme.md b/ms2query/readme.md
new file mode 100644
index 0000000..b2b1267
--- /dev/null
+++ b/ms2query/readme.md
@@ -0,0 +1,21 @@
+## Basic workflow (so far):
+This is for creating the database. This is not yet fully functional, so for now please use the notebooks/tutorial.ipynb if you already want to try out the prototype. 
+
+### Library generation
+```python
+from ms2query.create_new_library import create_new_library
+
+ms2query_lib = create_new_library(
+    spectra_files=["spectra.mgf"],
+    annotation_files=[],
+    output_folder="my_ms2query_folder/",
+    model_path="models/ms2deepscore.pt"
+)
+```
+
+### Loading already generated library
+```python
+from ms2query.create_new_library import load_created_library
+
+lib = load_created_library("my_ms2query_folder/")
+```
diff --git a/pyproject.toml b/pyproject.toml
index fbaac97..b416b53 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,7 @@ ms2deepscore= ">=2.6.0"
 rdkit= ">2024.3.4"
 nmslib= ">=2.0.0"
 umap-learn= ">=0.5.7"
+pyarrow= ">=14.0.1"
 
 [tool.poetry.group.dev.dependencies]
 decorator = "^5.1.1"
diff --git a/tests/helper_functions.py b/tests/helper_functions.py
index 82d1709..5c234b4 100644
--- a/tests/helper_functions.py
+++ b/tests/helper_functions.py
@@ -4,8 +4,8 @@
 import numpy as np
 from matchms.Spectrum import Spectrum
 from ms2deepscore.models import load_model
-from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet
-from ms2query.benchmarking.Fingerprints import Fingerprints
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.Fingerprints import Fingerprints
 
 
 TEST_RESOURCES_PATH = Path(__file__).parent / "test_data"
@@ -87,6 +87,36 @@ def get_inchikey_inchi_pairs(number_of_pairs):
             "CC(C)C[C@@H](C(=O)O)N",
             "L-Leucine",
         ),
+        (
+            "BSYNRYMUTXBXSQ-UHFFFAOYSA-N",
+            "InChI=1S/C9H8O4/c1-6(10)13-8-5-3-2-4-7(8)9(11)12/h2-5H,1H3,(H,11,12)",
+            "CC(=O)OC1=CC=CC=C1C(=O)O",
+            "Aspirin",
+        ),
+        (
+            "WHUUTDBJXJRKMK-VKHMYHEASA-N",
+            "InChI=1S/C5H9NO4/c6-3(5(9)10)1-2-4(7)8/h3H,1-2,6H2,(H,7,8)(H,9,10)/t3-/m0/s1",
+            "C(CC(=O)O)[C@@H](C(=O)O)N",
+            "L-Glutamic acid",
+        ),
+        (
+            "ZKHQWZAMYRWXGA-KQYNXXCUSA-N",
+            "InChI=1S/C10H14N5O7P/c11-8-5-9(13-2-12-8)15(3-14-5)10-7(17)6(16)4(22-10)1-21-23(18,19)20/h2-4,6-7,10,16-17H,1H2,(H2,11,12,13)(H2,18,19,20)/t4-,6-,7-,10-/m1/s1",
+            "C1=NC(=C2C(=N1)N(C=N2)[C@H]3[C@@H]([C@@H]([C@H](O3)COP(=O)(O)O)O)O)N",
+            "AMP",
+        ),
+        (
+            "GVJHHUAWPYXKBD-UHFFFAOYSA-N",
+            "InChI=1S/C2H6O/c1-2-3/h3H,2H2,1H3",
+            "CCO",
+            "Ethanol",
+        ),
+        (
+            "IKHGUXGNUITLKF-XPULMUKRSA-N",
+            "InChI=1S/C9H13NO3/c1-6(11)13-8-4-2-7(3-5-8)9(10)12/h2-6,11H,1H3,(H2,10,12)/t6-/m0/s1",
+            "C[C@@H](C1=CC=C(C=C1)C(=O)N)O",
+            "Salbutamol",
+        ),
     )
     if number_of_pairs > len(inchikey_inchi_pairs):
         raise ValueError("Not enough example compounds, add some in conftest")
diff --git a/tests/test_benchmarking/test_top_k_tanimoto_scores.py b/tests/test_benchmarking/test_top_k_tanimoto_scores.py
deleted file mode 100644
index 2c8ecd2..0000000
--- a/tests/test_benchmarking/test_top_k_tanimoto_scores.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import numpy as np
-import pytest
-from ms2query.benchmarking.TopKTanimotoScores import TopKTanimotoScores
-from tests.helper_functions import make_test_fingerprints
-
-
-def test_methods_top_k_tanimoto_scores():
-    scores = np.array([[1.0, 0.8], [0.9, 0.7], [0.8, 0.8]])
-    inchikeys_in_top_2 = np.array([["A", "B"], ["B", "C"], ["C", "A"]])
-    inchikeys = np.array(["A", "B", "C"])
-    top_scores = TopKTanimotoScores(scores, inchikeys_in_top_2, inchikeys)
-
-    assert top_scores.select_top_k_inchikeys_and_scores("A") == {"A": 1.0, "B": 0.8}
-
-    assert top_scores.select_top_k_inchikeys("A") == ["A", "B"]
-
-    assert top_scores.select_average_score("A") == pytest.approx(0.9)
-
-    assert top_scores.get_all_average_tanimoto_scores() == {"A": 0.9, "B": 0.8, "C": 0.8}
-
-
-def test_calculate_from_fingerprints():
-    fingerprints = make_test_fingerprints(nbits=5, nr_of_inchikeys=5)
-    top_scores = TopKTanimotoScores.calculate_from_fingerprints(fingerprints, fingerprints, 2)
-    assert top_scores.select_top_k_inchikeys_and_scores("AAAAAAAAAAAAAE") == {
-        "AAAAAAAAAAAAAD": 0.75,
-        "AAAAAAAAAAAAAE": 1.0,
-    }
diff --git a/tests/test_benchmarking/test_Fingerprints.py b/tests/test_ms2query_development/test_Fingerprints.py
similarity index 96%
rename from tests/test_benchmarking/test_Fingerprints.py
rename to tests/test_ms2query_development/test_Fingerprints.py
index 6ab8083..6bffc74 100644
--- a/tests/test_benchmarking/test_Fingerprints.py
+++ b/tests/test_ms2query_development/test_Fingerprints.py
@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
 from matchms.filtering.metadata_processing.add_fingerprint import _derive_fingerprint_from_inchi
-from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet
-from ms2query.benchmarking.Fingerprints import Fingerprints, get_similarity_matrix
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.Fingerprints import Fingerprints, get_similarity_matrix
 from tests.helper_functions import create_test_spectra, get_inchikey_inchi_pairs
 
 
diff --git a/tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py b/tests/test_ms2query_development/test_MS2DeepScoresForTopInChikeys.py
similarity index 78%
rename from tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py
rename to tests/test_ms2query_development/test_MS2DeepScoresForTopInChikeys.py
index f0ed9df..a065fed 100644
--- a/tests/test_benchmarking/test_MS2DeepScoresForTopInChikeys.py
+++ b/tests/test_ms2query_development/test_MS2DeepScoresForTopInChikeys.py
@@ -1,5 +1,5 @@
-from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet
-from ms2query.benchmarking.MS2DeepScoresForTopInChikeys import (
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.reference_methods.MS2DeepScoresForTopInChikeys import (
     calculate_MS2DeepScoresForTopKInChikeys_from_spectra,
 )
 from tests.helper_functions import create_test_spectra, ms2deepscore_model
diff --git a/tests/test_ms2query_development/test_ReferenceLibrary.py b/tests/test_ms2query_development/test_ReferenceLibrary.py
new file mode 100644
index 0000000..77b5bf9
--- /dev/null
+++ b/tests/test_ms2query_development/test_ReferenceLibrary.py
@@ -0,0 +1,62 @@
+import os
+import pandas as pd
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.Fingerprints import Fingerprints
+from ms2query.ms2query_development.ReferenceLibrary import (
+    ReferenceLibrary,
+    extract_metadata_from_library,
+)
+from ms2query.ms2query_development.TopKTanimotoScores import TopKTanimotoScores
+from tests.helper_functions import TEST_RESOURCES_PATH, create_test_spectra, ms2deepscore_model
+
+
+def test_run_ms2query():
+    model = ms2deepscore_model()
+    library_spectra = AnnotatedSpectrumSet.create_spectrum_set(create_test_spectra(nr_of_inchikeys=7))
+    test_spectra = create_test_spectra(1, nr_of_inchikeys=3)
+    library_spectra.add_embeddings(model)
+    fingerprints = Fingerprints.from_spectrum_set(library_spectra, "daylight", 100)
+    top_k_tanimoto_scores = TopKTanimotoScores.calculate_from_fingerprints(fingerprints, fingerprints, 3)
+    metadata_library = extract_metadata_from_library(
+        library_spectra,
+        [
+            "precursor_mz",
+            "collision_energy",
+            "compound_name",
+            "smiles",
+            "inchikey",
+        ],
+    )
+
+    results = ReferenceLibrary(model, library_spectra.embeddings, top_k_tanimoto_scores, metadata_library).run_ms2query(
+        test_spectra
+    )
+    print(results)
+
+
+def test_create_library(tmp_path):
+    lib_spectra = create_test_spectra(nr_of_inchikeys=10, number_of_spectra_per_inchikey=3)
+    # save_as_mgf(lib_spectra, os.path.join(tmp_path, "library_spectra.mgf"))
+    ms2deepscore_model_file = os.path.join(TEST_RESOURCES_PATH, "ms2deepscore_testmodel_v1.pt")
+    ReferenceLibrary.create_from_spectra(lib_spectra, ms2deepscore_model_file, tmp_path)
+    assert (tmp_path / ReferenceLibrary.embedding_file_name).exists()
+    assert (tmp_path / ReferenceLibrary.top_k_tanimoto_scores_file_name).exists()
+    assert (tmp_path / ReferenceLibrary.reference_metadata_file_name).exists()
+
+
+def test_create_and_use_library(tmp_path):
+    lib_spectra = create_test_spectra(nr_of_inchikeys=10, number_of_spectra_per_inchikey=3)
+    ms2deepscore_model_file = os.path.join(TEST_RESOURCES_PATH, "ms2deepscore_testmodel_v1.pt")
+    ms2query_library = ReferenceLibrary.create_from_spectra(lib_spectra, ms2deepscore_model_file, tmp_path)
+    test_spectra = create_test_spectra(1, nr_of_inchikeys=3)
+    results = ms2query_library.run_ms2query(test_spectra)
+
+    ms2query_library_2 = ReferenceLibrary.load_from_files(
+        ms2deepscore_model_file,
+        tmp_path / ReferenceLibrary.embedding_file_name,
+        tmp_path / ReferenceLibrary.top_k_tanimoto_scores_file_name,
+        tmp_path / ReferenceLibrary.reference_metadata_file_name,
+    )
+
+    results_2 = ms2query_library_2.run_ms2query(test_spectra)
+    pd.testing.assert_frame_equal(results, results_2)
diff --git a/tests/test_benchmarking/test_SpectrumDataSet.py b/tests/test_ms2query_development/test_SpectrumDataSet.py
similarity index 98%
rename from tests/test_benchmarking/test_SpectrumDataSet.py
rename to tests/test_ms2query_development/test_SpectrumDataSet.py
index 3e252eb..94baa36 100644
--- a/tests/test_benchmarking/test_SpectrumDataSet.py
+++ b/tests/test_ms2query_development/test_SpectrumDataSet.py
@@ -1,6 +1,6 @@
 import os
 import pytest
-from ms2query.benchmarking.AnnotatedSpectrumSet import (
+from ms2query.ms2query_development.AnnotatedSpectrumSet import (
     AnnotatedSpectrumSet,
 )
 from tests.helper_functions import create_test_spectra, ms2deepscore_model
diff --git a/tests/test_benchmarking/test_embeddings.py b/tests/test_ms2query_development/test_embeddings.py
similarity index 95%
rename from tests/test_benchmarking/test_embeddings.py
rename to tests/test_ms2query_development/test_embeddings.py
index 86fbbbd..5ad42c9 100644
--- a/tests/test_benchmarking/test_embeddings.py
+++ b/tests/test_ms2query_development/test_embeddings.py
@@ -1,6 +1,6 @@
 import os
 import pytest
-from ms2query.benchmarking.Embeddings import Embeddings, calculate_ms2deepscore_df
+from ms2query.ms2query_development.Embeddings import Embeddings, calculate_ms2deepscore_df
 from tests.helper_functions import create_test_spectra, get_library_and_test_spectra_not_identical, ms2deepscore_model
 
 
diff --git a/tests/test_benchmarking/test_methods.py b/tests/test_ms2query_development/test_methods.py
similarity index 78%
rename from tests/test_benchmarking/test_methods.py
rename to tests/test_ms2query_development/test_methods.py
index b584df4..240c11b 100644
--- a/tests/test_benchmarking/test_methods.py
+++ b/tests/test_ms2query_development/test_methods.py
@@ -1,6 +1,6 @@
 import numpy as np
-from ms2query.benchmarking.Fingerprints import Fingerprints
-from ms2query.benchmarking.reference_methods.predict_best_possible_match import predict_best_possible_match
+from ms2query.ms2query_development.Fingerprints import Fingerprints
+from ms2query.ms2query_development.reference_methods.predict_best_possible_match import predict_best_possible_match
 from tests.helper_functions import (
     get_library_and_test_spectra_not_identical,
 )
diff --git a/tests/test_benchmarking/test_predict_top_ms2deepscores.py b/tests/test_ms2query_development/test_predict_top_ms2deepscores.py
similarity index 90%
rename from tests/test_benchmarking/test_predict_top_ms2deepscores.py
rename to tests/test_ms2query_development/test_predict_top_ms2deepscores.py
index e830017..7cd00ce 100644
--- a/tests/test_benchmarking/test_predict_top_ms2deepscores.py
+++ b/tests/test_ms2query_development/test_predict_top_ms2deepscores.py
@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
-from ms2query.benchmarking.AnnotatedSpectrumSet import AnnotatedSpectrumSet
-from ms2query.benchmarking.predict_top_k_ms2deepscore import (
+from ms2query.ms2query_development.AnnotatedSpectrumSet import AnnotatedSpectrumSet
+from ms2query.ms2query_development.reference_methods.predict_top_k_ms2deepscore import (
     predict_top_k_ms2deepscores,
     select_inchikeys_with_highest_ms2deepscore,
 )
diff --git a/tests/test_ms2query_development/test_top_k_tanimoto_scores.py b/tests/test_ms2query_development/test_top_k_tanimoto_scores.py
new file mode 100644
index 0000000..e15bb15
--- /dev/null
+++ b/tests/test_ms2query_development/test_top_k_tanimoto_scores.py
@@ -0,0 +1,80 @@
+import numpy as np
+import pandas as pd
+import pytest
+from ms2query.ms2query_development.TopKTanimotoScores import TopKTanimotoScores
+from tests.helper_functions import make_test_fingerprints
+
+
+def test_methods_top_k_tanimoto_scores():
+    scores = np.array([[1.0, 0.8], [0.9, 0.7], [0.8, 0.8]])
+    inchikeys_in_top_2 = np.array([["A", "B"], ["B", "C"], ["C", "A"]])
+    inchikeys = np.array(["A", "B", "C"])
+    top_scores = TopKTanimotoScores(scores, inchikeys_in_top_2, inchikeys)
+
+    assert top_scores.select_top_k_inchikeys_and_scores("A") == {"A": 1.0, "B": 0.8}
+
+    assert top_scores.select_top_k_inchikeys("A") == ["A", "B"]
+
+    assert top_scores.select_average_score("A") == pytest.approx(0.9)
+
+    assert top_scores.get_all_average_tanimoto_scores() == {"A": 0.9, "B": 0.8, "C": 0.8}
+
+
+def test_calculate_from_fingerprints():
+    fingerprints = make_test_fingerprints(nbits=5, nr_of_inchikeys=5)
+    top_scores = TopKTanimotoScores.calculate_from_fingerprints(fingerprints, fingerprints, 2)
+    assert top_scores.select_top_k_inchikeys_and_scores("AAAAAAAAAAAAAE") == {
+        "AAAAAAAAAAAAAD": 0.75,
+        "AAAAAAAAAAAAAE": 1.0,
+    }
+
+
+@pytest.fixture
+def sample_scores():
+    """Creates a simple TopKTanimotoScores instance for testing."""
+    tanimoto_scores = np.array(
+        [
+            [0.9, 0.7, 0.5],
+            [0.8, 0.6, 0.4],
+            [0.95, 0.85, 0.75],
+        ]
+    )
+    top_k_inchikeys = np.array(
+        [
+            ["INCHI_A", "INCHI_B", "INCHI_C"],
+            ["INCHI_B", "INCHI_C", "INCHI_A"],
+            ["INCHI_C", "INCHI_A", "INCHI_B"],
+        ]
+    )
+    inchikey_indexes = np.array(["QUERY_1", "QUERY_2", "QUERY_3"])
+    return TopKTanimotoScores(tanimoto_scores, top_k_inchikeys, inchikey_indexes)
+
+
+# ----- save and load tests -----
+def test_save_creates_parquet_file(sample_scores, tmp_path):
+    sample_scores.save(tmp_path / "test_scores")
+    assert (tmp_path / "test_scores.parquet").exists()
+
+
+def test_save_creates_parent_directories(sample_scores, tmp_path):
+    sample_scores.save(tmp_path / "nested" / "dir" / "test_scores")
+    assert (tmp_path / "nested" / "dir" / "test_scores.parquet").exists()
+
+
+def test_roundtrip_produces_identical_object(sample_scores, tmp_path):
+    sample_scores.save(tmp_path / "test_scores")
+    loaded = TopKTanimotoScores.load(tmp_path / "test_scores")
+
+    assert loaded.k == sample_scores.k
+    pd.testing.assert_frame_equal(loaded.top_k_inchikeys_and_scores, sample_scores.top_k_inchikeys_and_scores)
+    assert sample_scores.select_top_k_inchikeys_and_scores("QUERY_1") == loaded.select_top_k_inchikeys_and_scores(
+        "QUERY_1"
+    )
+    assert sample_scores.select_top_k_inchikeys("QUERY_2") == loaded.select_top_k_inchikeys("QUERY_2")
+    assert sample_scores.select_average_score("QUERY_3") == pytest.approx(loaded.select_average_score("QUERY_3"))
+
+
+def test_roundtrip_accepts_string_path(sample_scores, tmp_path):
+    sample_scores.save(str(tmp_path / "test_scores"))
+    loaded = TopKTanimotoScores.load(str(tmp_path / "test_scores"))
+    pd.testing.assert_frame_equal(loaded.top_k_inchikeys_and_scores, sample_scores.top_k_inchikeys_and_scores)