Reducing the training set to 10 and 1% of its original size, aside from a general reduction in accuracy, also leads to a better relative performance of the r = 2 variant across all dimensions d (Figure ). These results suggest that choosing the r = 2 variant might be advantageous in low data settings, and there is no value in choosing r = 4 over r = 2 or r = 3, independent from d and the amount of available training data. However, as the r = 3 variant performed best in the case of the complete training set for high d, the r = 3 and d = 2048 variant is chosen for all further benchmarks, including reaction yield predictions. This result suggests that conceptual complexity, including learning, can be factored out of fingerprint creation, moving it instead to the classification task with a minor impact on classification performance. A non-learned fingerprint has the advantages of reducing bias and increasing the interpretability of results as each feature can be mapped to one or more molecular substructures.