The performance of each method is mostly consistent across different metrics, meaning that MVS-A provides the best performance both when considering the top 10% predictions, as indicated by relative precision and enrichment factor, and when evaluating the entire ranking, as measured by BEDROC. being less effective than random sampling of primary actives (Figure ). This could be due to the limited number of protein targets, assay technologies and compounds from which these rules were developed, making its application limited to a narrow range of HTS datasets. Alternatively, it could be related to the fact that PAINS can also flag frequent hitters, which might be considered true positives in this benchmark because of their dose-response activity, but might still be too promiscuous for further pharmaceutical development. Finally, it is also partially caused by the fact that PAINS filtering provides binary predictions instead of a rank estimate, thus making evaluation with metrics such as top-K precision or enrichment factor less comparable to other ranking methods.