For the drug discovery tasks, we noticed consistent decreases in MAE between 10% and 40% just by the inclusion of raw low-fidelity single-dose labels, with only a few exceptions (Supplementary Figures 2 and 7 to 9 for the remaining datasets). Moreover, we notice that augmenting with neural embeddings can outperform the actual low-fidelity labels by up to 10%. The predictions generated by neural readout low-fidelity models, either by themselves or in a hybrid setting, are generally slightly worse than the embeddings but comparable to the raw labels (Figure ). In contrast, the embeddings and predictions produced by sum readout low-fidelity models struggle to achieve any decrease in MAE, occasionally even degrading the performance. The inability of the sum readout low-fidelity models to fully model the data is particularly emphasised by the hybrid augmentation, where the actual experimentally-derived labels are used during training and the predicted ones for evaluation. The significant decrease in the effectiveness observed in this case indicates the disparity between the space covered by the raw labels and the information carried by sum readout low-fidelity models, which agrees with previous observations (Section 2.1). In addition, we evaluate fine-tuning strategies for sum and neural readout low-fidelity models. The former, without a learnable readout function and any frozen components, generally struggles to improve to the same degree or is even worse compared to other sum-based augmentations. In contrast, pre-training and fixing the graph layers, followed by fine-tuning of the neural readout generally matches the other neural-based augmentations and is even preferable by a large margin for datasets such as AZ-DR-R7, AZ-DR-R13, and AZ-DR-R14 (Figure ). To mimic the HTS setting on QMugs, we selected a diverse and challenging set of 10K molecules (Supplementary Notes 5.1). The QM models are of particular interest as certain quantum properties are naturally additive and non-local, such that the sum readout is theo-retically capable of modelling those tasks . While we explore this dynamic in , here we report similar trends as for drug discovery, namely that the adaptive embeddings and fine-tuning the adaptive readouts outperform the raw labels by a large margin, with the exception of the four dipole properties which are particularly difficult to learn (see Supplementary Notes 15 for a possible explanation). However, for a large selection of properties such as the HOMO and LUMO energies and rotational constants, the low-fidelity xTB labels are completely ineffective, whereas the adaptive readout strategies lead to improvements between 20% and 80% in MAE, depending on the properties. For additive properties such as the atomic, formation, and total energies, the sum readout is competitive, as expected, although it is still outperformed by adaptive readouts. In this transductive setting, for drug discovery and QMugs, we report a statistically significant relationship between the uplift in performance ('% MAE decrease') and linear correlation (Pearson's r) between the low and high fidelity measurements (Figure ).