More specifically, we use the sum operator as a representative of the standard readouts (performs similarly to mean and maximum on bioaffinity tasks ) and the Set Transformer as an adaptive (neural) readout . As a further quality check for the drug discovery tasks, we consider a 'null hypothesis' that predicts the dataset mean for each molecule, which is a relevant baseline because HTS data is biased toward low activity. We trained supervised VGAEs independently on all of the low-fidelity data for each multi-fidelity dataset. Our empirical results indicate that the performance of the sum readout models generally closely matches or even does worse than the null hypothesis (Figures and) for all drug discovery tasks, but is more competitive on the quantum tasks, particularly for the extensive and global properties such as the total energy. This agrees with the existing literature and highlights the unique challenge posed by the HTS data. For drug discovery, the adaptive readout models offer severalfold improvements in the capacity to learn from the low-fidelity data, as measured by the MAE (Figure ) and R 2 (Supplementary Figure ), with smaller but noticeable improvements for QMugs. The uplifts observed here translate to better downstream performance in both transductive and inductive settings, as can be seen from Section 2.2 onward. At the same time, models using adaptive readouts exhibit a characteristic structuring effect on the latent space with respect to the low-fidelity domain, as visualised using 3D UMAP (Figure ). For the drug discovery data, we notice a clear and often continuous demarcation of active and inactive compounds, an effect that is not present when using standard readouts, which generate scattered and less informative representations. Similar effects can be observed for quantum properties.