To train a machine-learning model on RBFE-Space, a graph neural network (GNN) approach was taken to describe molecular perturbations. This type of architecture was chosen because of its proven potential to learn molecular structures given enough data. One major advantage of learning directly the molecular topology instead of pre-computed molecular descriptors is that no prior knowledge of influential descriptors is required. However, when training complex models with many parameters (such as GNNs) care must be taken to provide a sufficiently large training domain to make sure that weights have been optimised to a point where an understanding of chemical structure (or chemical perturbation, in this work's case) has been reached. As RBFE-Space contains only 3964 points, we have opted for a pretraining and transfer-learning approach (figure ) which is a technique that has recently gained popularity in chemistry. In the pre-training phase, a cheaply-computed label, the relative estimated solubility (∆ESOL ) was computed for 1M randomly picked combinations of molecules in RBFE-Space and this training domain was used for pre-training the RBFENN model to learn molecular perturbations. Whereas any chemical descriptor could be picked for this application, ∆ESOL is a suitable candidate because it is a relatively complex descriptor which prevents the RBFENN from focussing its learning on a specific chemical detail which would likely happen when learning on simpler properties such as molecular weight or lipophilicity. The pre-training protocol in this approach showed sufficient learning convergence after 100 epochs of training, at which point training was interrupted; the runtime for this step was approximately 9h. After pre-training, the ∆ESOL training domain was discarded and the GNN layers' weights of the RBFENN (figure ) were 'frozen', i.e. their weights were not allowed to be adjusted during training. This transfer-learning phase thus started with a RBFENN architecture that had already learned molecular perturbations. RBFE-Space was then used as a training domain to train the non-frozen layers in the model to predict SEM. Whereas validation MAE varied across replicates, models were observed to converge to 0.4-0.5 kcal•mol -1 MAE. In this step, global minimum training MAE values are shown to be higher than global minimum validation MAE values (figure ). This is likely because of the reduced number of trainable parameters (only weights in 3E are trained) in combination with the low-data regime, where the validation set (20% of RBFE-Space) results in occasional small dynamic ranges, skewing statistics on this subset.