This section provides a comprehensive empirical analysis of all the transfer learning techniques described in Methods (Section 4.5). We evaluate the effectiveness of different feature augmentations for transfer learning given by: (1) explicit low-fidelity labels, (2) labels predicted by the low-fidelity models (with both sum and adaptive readout variants), (3) explicit low-fidelity labels during training and predicted ones during inference (a hybrid approach), (4) latent space embeddings generated by low-fidelity models (with both sum and adaptive readout variants) and two fine-tuning strategies tailored for graph neural networks: (5) pre-training and fine-tuning a standard GNN similarly to Smith et al. , and ( ) finetuning of the adaptive readout in models that were pre-trained on low-fidelity data, while keeping fixed the weights in other layers. All the methods are compared relative to baselines trained exclusively on sparse high-fidelity data as the goal is to improve their predictive ability in that setting. The effectiveness of different strategies is illustrated on a suite of experiments with the following structure and goals: