In previous work undertaken by our group as part of the SAMPL6 competition we observed that empirically correcting FEP-derived host-guest binding free energies by a linear regression model calibrated on preceding SAMPL5 submissions, led to significant decrease in mean unsigned error (MUE) of the predicted binding affinities. The present study extends this approach with machine-learning regression models that act as empirical correction terms to the FEP results. That is, the ML models are trained to predict the mistake compared to experimental values in Gibbs free energy that alchemical calculations make, referred to from here on as the ∆G of f set .