Although FEP/ML hybridisation does not appear to benefit RMSE scores in figure , the RMSE ranking for FEP/ML models appear to approach first place in the SAMPL4 challenge when trained on the full training set (595 compounds). The working model in figure is trained using a cross-validation approach which effectively limits training set sizes to 0.8 * 595 = 476 compounds which suggests that when generating a definitive ML correction term it would be preferable to use all 595 compounds as a training set. The offsets are transferable to a number of related SAMPL4 submissions