SISSO models were trained to predict the singlet excitation energy, which corresponds to the optical gap, the triplet excitation energy, the singlet-triplet gap, and the singlet exciton binding energy. In the following, the models generated by SISSO for each property are evaluated based on considerations of cost vs. accuracy, represented by Pareto plots. For accuracy, we consider both the cross validation "train" RMSE, obtained for the training set of 91 materials, and the "test" RMSE, obtained for the unseen validation set of 10 materials. In general, the computational cost tends to increase with the model complexity because the more complex models contain more primary features. The training set RMSE generally tends to decrease with the model complexity. The validation set RMSE is generally higher than the training set RMSE. For some of the more complex models the validation set RMSE is significantly higher than the training set RMSE, indicating over-fitting to the training data. For each property, we select the models that deliver the most robust performance out of each group of models with a similar computational cost. These are used to construct hierarchical screening workflows, as suggested in Ref. , in which a decreasing number of candidate materials are evaluated with increasingly accurate and more expensive models. Below, we discuss of the performance of SISSO for each property. Figure shows a Pareto plot of the accuracy vs the computational cost of the SISSO-generated models for the singlet excitation energy (optical gap). The equations of all the models, as well as tabulated values of their cost and accuracy are provided in the ESI. The models are clustered in three groups of lower, intermediate, and higher computational cost. In this case, even the models in the lower-cost group have a relatively high cost because all models contain crystal primary features. There is no model based only on single molecule primary features. In particular, all models contain the crystal triplet formation energy, ET C , whose computational cost is relatively high. The calculation of ET C is the main contribution to the computational cost of the lower-cost models. The models in the intermediate-cost group are more complex and contain a larger number of primary features, whose evaluation contributes to their computational cost. The higher-cost group contains only one model, M 4,3 , which is the most complex model, whose computational cost is high owing to the need to evaluate multiple primary features, including H ab , which is relatively expensive to evaluate.