To test the classification models, we looked at the area under the curve (AUC) of the receiver operating characteristic (ROC) on the BigBind test set. This gives an overall view of how well the model classifies actives from inactives. However, for practical virtual screening applications, one cares more about whether or not the model can select actives from a large set of mostly inactive molecules. To test this, we evaluated the final ligand-and-receptor classification model on LIT-PCBA, a challenging benchmark composed of data from highthroughput screens. LIT-PCBA includes lists of experimentally verified active and inactive molecules for all 13 targets, each of which has several 3D structures cocrystallized to different ligands. We use the ligands in these cocrystal structures to define the binding pocket in the same way we created all the pockets in the BigBind dataset. We emphasize that, since all the targets from LIT-PCBA were placed in the BigBind test set, each target is entirely new to Banana. We measured the model's top 1% enrichment factor (EF1%) and normalized enrichment factor (NEF1%) on each target in LIT-PCBA. The EF1% is the ratio of actives in the top 1% of ranked compounds divided by the ratio of actives in the whole set. The NEF% is the EF1% divided by the maximum achievable EF1% for the target, thus normalizing the value between zero and one. These results were compared with AutoDock Vina 1 and Gnina, as reported by Sunseri and Koes . We wish to emphasize that, though LIT-PCBA is a promising benchmark due to its use of experimental results rather than putative decoys, many of the assays used are cell-based rather than target-specific. Thus the activity annotations are noisy and not entirely dependent on the molecule's engagement with the protein target in question.