The reliance on BA as the key metric of choice to characterize the accuracy of classification QSAR models can be justified by the historical use of these models for lead optimization, where the aim has been to refine or design small sets of compounds to enhance the activity of a parent molecule. The possibility of using QSAR models for virtual screening has been discussed in the literature as well but practical utility of such applications has been limited by relatively small size of both training set and virtual screening libraries. However, continuing rapid growth of expansive chemical bioactivity databases like ChEMBL 9 and PubChem 10 , along with the exponential growth of make-on-demand chemical libraries such as eMolecules Explore and Enamines REAL Space , has significantly increased the appeal of using QSAR models as an alternative to structure based methods in high throughput virtual screening (HTVS). In an HTVS campaign, these models can be used to screen ultra-large, multi-billion compound libraries, with the goal of nominating a small number of hit compounds for experimental validation . Notably, false positive experimental nominations are expensive, both in terms of compound acquisition (synthesis or purchase) and screening biological screening costs. These considerations underscore the critical importance of employing QSAR models with high positive predictive value (PPV, often called precision) to nominate hit compounds in HTVS campaigns as, for practical considerations, regardless of the size of the virtual screening libraries, only small fraction of compounds can be selected for the experimental validation. Indeed, the high value of this metric calculated for the selection of computational hit compounds implies high enrichment for active compounds, or, conversely, low rate of false positives, among the relatively small sets of nominated molecules.