The three ML methods we benchmarked are all able to match or exceed the accuracy of the linear regression on the vacuum TD-DFT result, but their ability to do so is heavily influenced by the strategy used to split the data into training, validation, and test sets (Figure ). All three methods achieve a MAE under 10 nm when splitting by dye-solvent ), but this is a better assessment of the ability of the model to generalize and is thus more reflective of performance for de novo design tasks. When using the random splitting strategy, dye molecules that appear in both the train and test set in different solvents have narrower error and ensemble variance distributions than those that only appear in the test set (Figure ). As shown in Figure The improved generalizability of the two D-MPNN approaches over the GBRT method may be the result of the automatically-learned dye representations. ChempropMultiFidelity outperforms Chemprop because in addition to this automatically-learned representation, it also incorporates additional physical knowledge through the inclusion of a predicted TD-DFT value in the learned embedding. It should be emphasized that this predicted TD-DFT value comes from an additional Chemprop model rather than an actual TD-DFT calculation, so there is no need to perform an additional calculation to predict on an unseen dye molecule. The ChempropMultiFidelity method achives a MAE of 18.3 nm on scaffold splits, an improvement over the TD-DFT plus linear regression approach at a fraction of the cost.