To illustrate the importance of leveraging low-fidelity measurements in small-sample regimes characteristic of high-fidelity tasks, we evaluate the previously discussed transfer learning strategies while varying the training set sizes of high-fidelity data and maintaining fixed validation and test sets in the same highfidelity domain. We selected the largest drug-discovery dataset (AZ-DR-R2), having slightly under 12, 000 molecules in the high-fidelity domain (confirmatory screen) and assembled random training subsets of sizes 1K, 2.5K, 5K, and 10K, such that larger training samples contain all the molecules of the smaller subsets, with fixed validation of test sets of equal sizes from the remaining data outside the 10K training set. For QMugs, we used the same diverse set of 10K molecules with high-fidelity DFT labels as a starting point and challenging train, validation, and test splits of size 8K, 1K, and 1K respectively (Supplementary Notes 5.1). We then generated high-fidelity training subsets of sizes 25K, 50K, 100K, and 300K following the same strategy as for drug discovery, keeping the validation and test sets of size 1K fixed. We demonstrate the effects of transfer learning in both transductive (low-fidelity datasets contain the molecules appearing in the high-fidelity subsets) and inductive settings (adjusted low-fidelity datasets where the molecules appearing in high-fidelity subsets are removed). For brevity, we illustrate only the transfer by embeddings strategy, generated by low-fidelity models trained in either transductive or inductive settings.