Table Recall statistics for CSLVAE on the three trillion compound UnREAL library. We sampled ten thousand compounds uniformly at random over the library, encoded with the CSLVAE encoder, and then decoded with the CSLVAE decoder using the argmax decision rule for 1, 10, 100, and 1000 retrievals. We report the percent of compounds which were accurately reconstructed. 25 remained positive (enriching) for NGT and turned negative (depleting) for HE for both targets. The reduced HE enrichment compared to NGT could indicate that AtomNet bioactivity scores of partial compounds translated non-additively to full compounds. For example, a fragment could be scored as active whereas any expansion along vectors dictated by the caps were poorly tolerated. The performance degradation for HE is more pronounced in ASAH1 than in CDK5 likely because abundant training data for kinases led to better recognition of common ATP-site binding motifs compared to hydrolase binding motifs. Both approaches remained enriching relative to a random selection. Qualitatively, the potency results (bottom panels) mirror those of bioactivity but we observed much higher levels of enrichment, (NGT: EF MEL 25 = 98-fold (ASAH1); 262-fold (CDK5); HE: EF MEL 25 = 530-fold (ASAH1); 1,033-fold (CDK5)). Notably, and contrasting the bioactivity results, HE showed higher enrichment for ASAH1 and CDK5 than NGT when chemical diversity was disregarded. However, when corrected for chemical diversity, NGT demonstrated superior enrichment, consistent with the normalizing flow policy approximating a distribution covering multiple modes that satisfy the constraints. These results suggest that it is more challenging to enrich for activity, i.e., distinguishing active from inactive compounds, than enriching for potency. The AtomNet ® regression model to predict potency was not trained on inactive compounds, so it lacks familiarity with phenomena like activity cliffs. Many compounds can possess favorable molecular interactions in the binding site despite lacking activity.