We conclude this overview of our empirical study by highlighting two of the main reasons behind our extensive suite of evaluated augmentations (corresponding to points (1) -( ) above): i) there are multiple possible ways of transferring knowledge from separately trained low-fidelity models and some might be more adequate for certain protein targets or quantum properties, dataset sizes, or fidelity correlations; ii) every time an adaptive (neural) readout is used, we also report the result for the equivalent model with a sum readout (in an attempt to simulate an approximately similar inductive bias in the feature extraction part of graph neural networks). The proposed augmentations are agnostic to the underlying architectures and, thus, the main goal of these baselines is to illustrate the limited potential of standard GNN readout functions for transfer learning in multi-fidelity settings.