In terms of false positive variety, both machine-learning based approaches yield a more diverse selection of interferents, strongly peaking between 90% and 95% scaffold diversity, while PAINS centers around 80% (Figure ). This advantage is due to the fact that the GBM-based approaches do not rely on the presence of specific molecular motifs However, the opposite happens for true positive discovery, where GBM-based approaches tend to yield less diverse selections of true hits, centering around 60% scaffold diversity (Figure ). In this case, the true positives identified by MVS-A and CatBoost are the ones that fit well the learned class boundary between actives and inactives in the primary data. The boundary in this case tends to include only a limited region of the chemical space, leading to more structurally similar true actives. In contrast, primary readout ranking has no chemical bias in its selection criteria, thus retrieving more diverse true positives.