Popular best practices for QSAR model development and validation have traditionally emphasized both the challenges associated with model development for imbalanced datasets and the use of balanced accuracy (also known as Correct Classification Rate) as a key desired metric to characterize the performance of QSAR models . Given the natural imbalance in HTS datasets, highly skewed towards inactive molecules, balancing these datasets through undersampling (see, for instance, a recent rigorous study ) has been a conventional method to preprocess such datasets prior to building models with the accuracy defined using BA or AUC metrics. These conventional practices have been appropriate in the past when training sets had relatively smaller size, virtual screening libraries have been relatively small, and the key context of use for QSAR models related to the task of hit or lead optimization. Furthermore, dataset balancing, BA-driven model selection, and recommended use of conservative applicability domains of QSAR models limited the selection of compounds from external libraries to relatively small sets expected to include roughly the same ratio of active and inactive molecules as in the training sets, which justified the use of models with high BA to assess the external sets.