For all four properties, the workflows based on SISSO mod-els can effectively screen out most of the materials that are not of interest. However, the classification performance varies across properties and screening targets in terms of the number of false positives that pass the workflow and the number of true positives missed. The workflows for the optical gap and triplet excitation energy yield a more robust performance than the workflows for the singlet-triplet gap and exciton binding energy. We attribute this to the overall narrow energy range of the singlet-triplet gap and exciton binding energy in the PAH101 dataset, as well as the small number of samples with the desirable low values. Additional data acquisition would be required to train better ML models. To this end, the present models could be used to select materials likely to have low singlet-triplet gaps or low exciton binding energy for additional GW +BSE calculations. The SISSO models for the triplet exciton energy performed particularly well and the lowest cost model, which only requires evaluating single molecule DFT features, successfully classified all the materials with triplet exciton energies in the IR with a very small number of false positives. We note that ML models trained on PAH101 are not guaranteed to perform well for materials that are significantly different chemically or structurally. Therefore, we recommend validating the performance of these models for materials outside of the PAH101 set before using them for large scale materials screening.