Gaussian Process (GP) models were fitted separately for each molecule, to a training set of dimers from the low energy predicted crystal structures, selected to be maximally separated in discriptor space, using atomic symmetry functions as structural descriptors of the dimers and the max-min algorithm for selection of the dimers included in the training set (for which MP2 calculations are performed). Our previous work demonstrated that such an approach to training point selection yields improved results compared to random selection of training data. As the purpose of the GP model is to reduce the computation time associated with the QM dimer energy correction, only training fractions of 40% and lower were considered when training the models; we refer to the resulting models as FIT+DMA+GP hereafter.