  Structure-based virtual screening (SBVS) is a key workflow in computational
drug discovery. SBVS models are assessed by measuring the enrichment of known
active molecules over decoys in retrospective screens. However, the standard
formula for enrichment cannot estimate model performance on very large
libraries. Additionally, current screening benchmarks cannot easily be used
with machine learning (ML) models due to data leakage. We propose an improved
formula for calculating VS enrichment and introduce the BayesBind benchmarking
set composed of protein targets that are structurally dissimilar to those in
the BigBind training set. We assess current models on this benchmark and find
that none perform appreciably better than a KNN baseline.
