fine-tuning strategies involving adaptive readouts. In drug discovery tasks, on the other hand, the standard/vanilla graph neural networks significantly underperform the baselines. For completeness, we also provide comparisons with state-of-the-art strategies for multi-fidelity learning and transfer learning with graph-structured data, in the form of the multi-fidelity state embedding (MFSE) algorithm proposed in and a variation of pre-training and fine-tuning devised by . Unfortunately, neither of the two approaches performs particularly well on drug discovery tasks, further justifying our approach to transfer learning with graph neural networks. We take several steps towards ensuring that our framework is general by firstly extending to a scenario with real-world data that has more than two levels of fidelity. Furthermore, for this setting we also evaluate a model operating directly on 3D molecular structures (the SchNet architecture ) in place of a typical graph neural network, thus validating transfer learning with adaptive readouts on this family of widely-used and state-of-the-art models as well.