The top-performing FEP/ML (SVM; MolPropsAPFP)and ML (SVM; MolProps) models were introduced in the SAMPL4 challenge retrospectively (figure ) to correct the results of SAMPL4 submission 004 that featured a FEP protocol most similar to the one used to generate calculated FEP values in FreeSolv. In line with the results obtained on the Free-SolvSAMPL4 test set, FEP/ML SVM models trained with MolPropsAPFP outperformed standalone FEP for all SAMPL4 statistics. For all metrics the gains are significant, moving the FEP/ML prediction to 1st or 2nd rank as judged by MUE, r or Kendall tau metrics, and from 28th to 4th position as judged by RMSE. Many of the top-performing methods have very similar performance within statistical uncertainties, so care must be taken not to overinterpet changes in rankings. Nevertheless it is clear that the ML-derived correction terms improve the accuracy of the FEP methodology.