This multi-fidelity approach improved the model generalizability and improved performance for more rigorous splitting strategies. Our best model achieved a MAE of less than 7 nm on a held-out test set from a random split of dye-solvent pairs in the Deep4Chem dataset, and near 14 nm and 19 nm when splitting by dye molecule and dye scaffold, respectively. This is substantially better than the predictions of TD-DFT calculations alone, and at much lower cost. Our multi-fidelity approach has the advantage that the lower-fidelity data can cover a larger area of chemical space than the higher-fidelity data. Future work should compare this approach to additional methods for training ML models on multi-fidelity data, such as transfer learning (e.g. Figure ), imputation, ∆-ML, and multi-target weighted-lossfunction approaches.