Overall, we demonstrate that a model trained on this dataset can successfully generalize to new targets and shows promise for prospective virtual screening campaigns. We hope that newer, more advanced, models will use BigBind to achieve even greater performance. To generate the BigBind dataset, we first determined the Uniprot 34 accession numbers for each protein in the CrossDocked dataset using the SIFTS dataset. The receptor structures from CrossDocked are clustered into conserved binding sites from Pocketome,; we filtered out all proteins with multiple such binding sites. We then queried the ChEMBL 33 database to find all molecules with known activities for those proteins. We only used assays with target type of "PROTEIN" to ensure each measurement was target-specific rather than cell-based. We assumed that all compounds in ChEMBL that are annotated as active against those proteins bind in that known pocket (this assumption may be incorrect for some compounds, leading to noise in the dataset). We also note that we used the pChEMBL value as an activity score for each protein-ligand pair; this adds additional noise because it aggregates different types of experimental information (such as K i , K d , and IC50) into a single value. To reduce this noise in the dataset, we additionally removed data from primary high-throughput screening (HTS) assays (standard type equal to "Potency").