To further maximise the RBFENN SEM predictivity, a fine-tuning phase was performed where all weights of the RBFENN were 'unfrozen', i.e. all weights were allowed to be adjusted during training. The idea behind this approach is that the GNN component of the RBFENN can further optimise its SEM predictivity in unison with the remaining layers of the model. validation MAE started increasing). For each replicate, the bestperforming (i.e. lowest validation MAE) model at epoch n was extracted and used as the final model. In cases were fine-tuning showed no decrease in validation MAE over the best model in the transfer-learning phase, the top-performing model of the transferlearning phase was used.