We compare the performance of three types of models, each corresponding to one of the dye representations described above. The FSD representation was used with the gradient boosted regression tree (GBRT) algorithm 50 as implemented by Ju et al. For both D-MPNN representations described above, the resulting fingerprint embedding is passed to a feed-forward neural network (FFNN) to accomplish the regression task. The three types of models are illustrated in Figure . The ChempropMultiFidelity model is a variation of the hybrid physics-ML models reviewed by Jia et al. D-MPNN hyperparameters (including hidden sizes, numbers of layers, dropout, batch sizes, learning rates, and warm-up epochs) were tuned using SigOpt. The GBRT used the