This advantage stems from the fact that in the FEP/ML approach the ML models only need to learn to correct errors in the FEP calculations, whereas in a pure ML approach the models must learn the physics of hydration. Another advantage of FEP/ML is that the hydration free energies of individual compounds are predicted with precision similar to that of the FEP calculations, whereas ML-based predictions by ensemble of identical models show more significant variability. In a retrospective analysis of all SAMPL4 submissions, the accuracy gains obtained in FEP/ML are sufficient to propel a mid-ranked FEP protocol to among the top-ranked submissions. Further, the accuracy improvements are not limited to a single simulation protocol, and a number of related FEP approaches benefit from the correction terms. This likely stems from the fact that the hydration free energies predicted by a number of forcefields and software show correlations in their outliers. However the performance of the correction terms is expected to decrease the more the simulation protocol diverges from that used to generate the training set.