An increasingly popular way to reduce computational time in P-L binding affinity prediction and ranking, as well as for VS purposes, is the use of SFs based on Machine Learning (ML). ML-based SFs have been shown to outperform classical SFs in terms of accuracy and computational time and can reduce the number of false positives . There are a number of key problems associated with the use of ML-based SFs, which have not been properly addressed yet: their lack of interpretability, and overfitting, due to the lack of proper benchmarks. Another important issue that limits the potential of ML-based SFs is that only a few of them have been integrated into popular docking tools, and most are being used merely as rescoring tools .