Table presents the performance metrics for gradient boosting tree models developed for five distinct datasets, each analyzed under balanced and imbalanced training conditions. The datasets were evaluated for the number of active compounds correctly identified within the top 128 selections (referred to as PPV-128), alongside other performance metrics described above, including BA, AUC, BEDROC with varying alpha levels, and PPV. Our findings indicate a significant increase (approx. 2-fold) in PPV-128 for models with high PPV built with the original, imbalanced datasets, emphasizing the advantage of such models for the use in HTVS campaigns. Using the PPV-128 as a baseline for true model performance at the HTVS task, we evaluated the conventional QSAR model assessment metrics (Table ). BA stood out as the worst, having an inverse relationship with the number of nominated actives. This outcome reinforces the assertion that, although BA is a widely recognized metric of QSAR model accuracy, it is not the most appropriate metric for evaluating HTVS performance of the models. AUC demonstrated better performance, marginally favoring imbalanced models over balanced ones. However, it fails to truly highlight the scale at which one model outperforms the other at the HTVS task, and its reliability diminished for two assays, aid_485341 and aid_624202. The BEDROC metric was originally proposed to address the issue of assessing early enrichment of models, specifically for virtual screening, and requires a tunable alpha parameter . BEDROC with an alpha parameter set at 100 proved more effective than AUC in correlating with the performance of imbalanced models. Yet, like AUC, it fails to differentiate the more effective model for assays aid_485341 and aid_624202. Further, the effectiveness of BEDROC notably declined when the alpha was reduced to 20. This pattern suggests that BEDROC's performance heavily relies on carefully adjusting its alpha setting based on the dataset. This need for precise tuning makes BEDROC more complex and less userfriendly than other, more straightforward metrics. PPV stands out as able to differentiate the better preforming imbalanced models on all datasets while requiring no tunable parameters and a simple calculation and interpretation.