predicted by an auxiliary model to using true TD-DFT values. As shown in Figure , the detrimental effect of the noise introduced using the D-MPNN TD-DFT feature may be outweighed by the dramatic savings in computational cost and time from no longer needing to perform anew TD-DFT calculation for an unseen molecule. In our comparison of four different solvent representations, we found that none substantially and consistently outperform the others. It is necessary to represent the solvent in some way to achieve good predictions, but the CGSD, Minnesota descriptors, Morgan fingerprints, and SolventMPNN approaches all achieve similar results. The Morgan fingerprint and SolventMPNN approaches do, however, have the advantage that they are computable for any solvent since they are not restricted to look-up tables as are the CGSD and Minnesota methods. Chemprop outperforms GBRT across all datasets. ChempropMultiFidelity is the best performer on all datasets except CDEx, for which all methods show substantially worse performance compared to the other datasets.