Supervised pre-training and fine-tuning is a transfer learning strategy that has previously proven successful for non-graph neural networks in the context of energy prediction for small organic molecules. In its simplest form, and as previously used by Smith et al. , the strategy consists of first training a model on the low-fidelity data D S (the pre-training step). Afterwards, the model is re-trained on the high-fidelity data D T , such that it now outputs predictions at the desired fidelity level (the fine-tuning step). For the fine-tuning step, certain layers of the neural network are typically frozen, which means that gradient computation is disabled for them. In other words, their weights are fixed to the values learnt during the pre-training step and are not updated. This technique reduces the number of learnable parameters, thus helping to avoid over-fitting to a smaller high-fidelity dataset and reducing training times. Formally, we assume that we have a low-fidelity predictor fS (corresponding to pre-training) and define the steps required to re-train or fine-tune a model fT0 into a high-fidelity predictor fT W S = Weights( fS ) (Extract weights of pre-trained model fS )