over 8 times over the baseline R 2 (0.455), and by 37.39% over the raw labels. The adaptive embeddings produced in an inductive setting generally match the raw labels, even outperforming them for the 1K training set size. Both sum embeddings perform similarly, improving upon the baseline but not matching the raw labels. We notice similar trends for different quantum properties. For example, in a transductive setting, MAE is almost halved for LUMO energy, and it is reduced by 4 to 8 times on the total and atomic energies and the rotation constants for the most relevant 8K training split. Interestingly, for that split the embeddings learnt in the significantly more challenging inductive setting often outperform the actual low-fidelity label. Depending on the quantum property, these observations can generalise even to the splits that use more high-fidelity training data (e.g. for total and atomic energies, rotational constants, and LUMO and HOMO energies to an extent). Representations learnt by sum readout low-fidelity models can lead to an improvement compared to the baseline models (no augmentation), however by a much smaller amount.