The GAP was trained using just 3300 (MP2/6-311G(2d,p)) QM calculations in total. In comparison, computation of the thermodynamic quantities reported in this paper required around 10 8 evaluations of the ligand intramolecular energetics, which would have been infeasible using even a very inaccurate QM/MM approach. Interestingly, using the FEP set-up described here (Supporting Information), GAP is only a factor of three times slower than the OPLS force field. There are two reasons for this. First, the speed of machine learning potentials are usually compared with evaluation of force field terms for small molecules, whereas in the condensed phase, long-ranged electrostatic interactions become a significant computational overhead. Second, the ligand is not moved at every Monte Carlo step in the condensed phase, so evaluation of the intramolecular energetics can be skipped when not required. The second version of the GAP is able to reproduce QM energies to a high accuracy of close to 1 kcal mol -1 following training on a gas phase QM MD data set, supplemented by configurations of the ligand taken from the binding site of leukotriene A4 hydrolase. We envisage iterative fitting approaches such as this being a key feature of future machine learning potentials to fill any gaps in the training data, especially if corrections can be automated and made on-the-fly. It is encouraging that substantial improvements could be made to version 2 of the GAP with only 300 extra training configurations and minimal changes to its behavior in the water and p38 kinase environments.