We have leveraged several recently-published datasets to benchmark models in their prediction of the peak wavelength of maximum absorption for dye molecules. Our results showed that D-MPNN models outperformed the best known fixed-fingerprint regression tree method, and the performance gain was more pronounced when we used more rigorous splitting strategies to evaluate the generalizability of the models to unseen chemistries. We also developed a multi-fidelity method for incorporating data from TD-DFT calculations to improve the accuracy of experimental predictions. Vertical excitation energies from gas-phase TD-DFT calculations have a good linear correlation with the experimental peak positions (MAE = 24 nm) of dyes (measured in various solvents). TD-DFT PCM calculations in the solvents do not correlate as consistently with the experimental data.