Here, we reconsider and revise these previous best practices and recommendations driven by the considerations outlined above that in the modern age of medicinal chemistry we face large and typically, highly imbalanced biological screening datasets and huge compound libraries accessible for virtual screening. It is thus more natural to acknowledge that both training sets and virtual screening sets are highly imbalanced in favor of inactive compounds, and new principles of model building and accuracy metrics need to be emphasized.