For all categories of datasets (originating from public or private sources; regression and/or classification tasks), we report substantial increases in performance when using adaptive readouts, often between 20% -40% in MAE and up to 100% in R 2 (Figure ), with a remarkable uplift for the classification task AZ-DR-C2, where the MCC increased from 0.69 to its maximal value of 1.0. We are again able to identify that the embeddings produced by sum readout low-fidelity models lead to performance degradation compared to the plain non-augmented baseline. While the augmentations given by the sum readout models carry some low-fidelity information, their effectiveness is further limited by a different problem. Namely, for AZ-DR-R11 we selected the most active compound in the test set (Figure ) and supplied the high-fidelity models with simulated low-fidelity labels (i.e. Z-Score values ranging from -50 to 50 in 0.5 increments). We leveraged models augmented with predicted labels as we can directly provide artificial values, unlike for the variation with embeddings. We discover that models that rely on sum readout-based low-fidelity models fail to learn a meaningful relationship between the provided label and the target pIC50, as the pIC50 value steeply increases to unlikely quantities even for common Z-scores in the range of -20 to -30.