In this study, we demonstrate that the common practice of balancing training sets to achieve models with high BA is not optimal to address the primary goal of HTVS, which is to enhance the proportion of active compounds identified in, by necessity, small selections of virtual screening hits . We further show that forfeiting data balancing prior to model development results in models that achieve higher PPV calculated for hit selections from large external libraries. As a proof of concept, we have employed five large, imbalanced, and diverse chemical datasets to demonstrate the difference in simulated HTVS performance for models characterized by both conventional (BA or different types of AUC metrics) and PPV. Our results show that training set balancing, as expected, increases BA while lowering the PPV. However, and importantly, we also show that roughly 30% more true positives are present in the top 128 (equivalent to the number of compounds in one plate using in experimental HTS) or 256 (two plate) hit nominations made by models with the highest PPV compared to the selections recommended by models with the highest BA across all five datasets. These findings strongly advocate for a paradigm shift in recommended best practices for constructing QSAR models to be used in HTVS of ultra-large libraries. We recommend that QSAR practitioners, especially those focused on early-stage hit discovery, prioritize models with high PPV over those with high BA for the use in practical HTVS campaigns. Furthermore, reflecting on recent observation concerning broadening use of QSAR modeling approaches in many areas of research , we posit that approaches advocated herein can find broader application in many areas of study that employ virtual screening of external collections of objects (e.g., environmental chemicals or potential patient populations).