Taking PPV as a good metric to assess HTVS performance, we expanded our analysis to include the PPV specific to the nominated set of N compounds. We refer to this metric as PPV-of-the-top-N, and represents the proportion of active compounds within the nominated set relative to the total number of nominations. This makes it distinct from other early enrichment metrics, like Enrichment Factor or Robust Initial Enhancement, that are dependent on the proportion of actives compounds in the training set. PPV-of-the-top-N is invariant to this proportion, as long as N is smaller than the number of actives present, since it only considers the small nominated set and nothing else. This makes PPV-of-the-top-N directly pertinent to nomination-constrained HTVS campaigns, as synthesis and experimental characterization will only be pursed for the top-N nominated compounds with the other prediction being ignored. We evaluated the models using this metric across a spectrum of N values, ranging from 16 to 1000. Our findings revealed that, with the exception of aid_485341, imbalanced models consistently surpassed their balanced counterparts in performance, particularly at N=128 and N=256 which correspond to the capacities of one or two 1536-well qHTS plates (Figure ). This observation further challenges the traditional QSAR modeling practices that advocate for dataset balancing, suggesting that such an approach may not confer an advantage in HTVS contexts. The present study has critically examined the common QSAR modeling practice of balancing datasets to achieve models with improved balanced accuracy (BA). While we think that this traditional approach remains useful for historically common application of QSAR models to hit and lead optimization, our investigation suggests that contrary to traditional approaches, prioritizing high positive predictive value (PPV) over BA is more effective for high throughput virtual screening (HTVS) applications. The results from five large, imbalanced, and diverse chemical datasets demonstrate that imbalanced models, which forgo the balancing of training sets, not only increase PPV but also significantly enhance the number of true positives within the top selections of virtual screening hits. Our findings also highlight the shortcomings of BA as a performance metric in HTVS, as it does not reliably correlate with the identification of true actives, contrary to the objectives of HTVS. In comparison, while other common metrics like AUC and BEDROC provided some insight, they fell short in clarity and ease of use compared to PPV. This study thus recommends a shift in QSAR modeling for HTVS to assess model performance utilizing PPV, or more specifically PPV of the top N nominated compounds, aiming to reduce false discoveries and their costs in compound nomination.