In this section, we systematically evaluate the performance of all the proposed low-fidelity augmentations on our HTS-based drug discovery collection and on QMugs in the transductive setting (i.e. real, experimentally-derived low-fidelity labels are available for drug discovery, with semi-empirical xTB labels for QMugs), with a standard train, validation, and test evaluation workflow (Figure ). The transductive setting acts not only as an excellent machine learning benchmark, but is also of practical use in drug discovery as it enables higher quality predictions in the high-fidelity domain for the millions of compounds that are present in the screening cascade. To this end, we train models on sparse high-fidelity data and incorporate different representations of low-fidelity information (strategy 1, and strategies 2 -4 with both sum and neural readout variations), or fine-tune the low-fidelity models from Section 2.1 (strategies 5 and 6). The number of times each strategy ends up as the best performing one is provided in Table .