This work has demonstrated that it is possible to combine 'physics-driven' FEP methods with 'data-driven' machine learning methods to predict absolute hydration free energies of small molecules. The chief advantage over FEP is that improvements in the accuracy of the predictions are achieved without having to embark in cumbersome forcefield parameterisation efforts. When compared with ML, the FEP/ML approach outperforms FEP with a much smaller training set size. This is significant as it indicates that for a new dataset it is possible to make predictions without any available experimental data initially, and switch to an FEP/ML approach once a number of data points have been experimentally determined.