Figure shows the predictions of the SISSO models selected based on considerations of cost and accuracy as a function of the GW +BSE reference values of the singlet exciton binding energy. Parity plots for all other SISSO models are provided in the ESI. We note that, similar to the singlet-triplet gap, the energy range of the GW +BSE reference data for the singlet exciton binding energy is significantly smaller than the energy range of the singlet and triplet exciton energies (see the distributions in Figure ). Therefore, the deviations of the SISSO model predictions appear larger in comparison to the reference data even though they are of comparable magnitude or even smaller than the deviations of the SISSO models for the singlet and triplet exciton energies. Terphenyl is a major outlier to the point that it noticeably skews the test RMSE. This is, perhaps not surprising because terphenyl is an outlier for most other properties considered here. Some of the outliers encountered for other properties are also outliers for the singlet exciton binding energy models, including PUJQIV, FACPEE, and GUQZUP. β -Tribenzopyrene (CSD reference code TBZPYR), dinaphtho(1,2-a:1',2'-h)anthracene (CSD reference code DNAPAN), 9,10-diphenylanthracene (CSD reference code DPANTR01), and anthracene (CSD reference code ANTCEN) are not structurally or chemically distinct from most of the materials in the PAH101 set. For M 1,1 a relatively high value of the single molecule ionization potential, IP S , combined with a low crystal density, ρ C can lead to significant overestimation (e.g., for terphenyl, anthracene, and DNAPAN). Conversely, a low IP S value combined with a high ρ C can lead to significant underestimation (e.g., for TBZPYR, GUQZUP, PUJQIV, and FACPEE). However, some of these remain outliers for the more complex models, which include additional primary features. It is possible that the relatively large number of persistent outliers is a reflection of the difficulty of training reliable models to predict very small target values. Figure demonstrates a three-stage screening workflow constructed based on the SISSO models selected for the singlet exciton binding energy. A small exciton binding energy means it is easier to separate excitons into free charge carriers. It is also often associated with strong dielectric screening and better charge transport. For demonstration purposes, we screen for materials with a singlet exciton binding energy below 0.3 eV. There are 14 such materials in total in the PAH101 set. The screening thresholds for each stage of the workflow are set to 0.3 eV + n× RMSE, where n=1,2,3 and RMSE refers to the training set RMSE of each model. With n=1, the first-stage model, M 1,1 , screens out many of the materials, whose exciton binding energy is above 0.3 eV, leaving 20 false positives. However, 4 of the 14 materials with exciton binding energy below 0.3 eV are also screened out, leaving 10 true positives. Further screening by the second-stage model, M 2,2 , and subsequently the third-stage model, M 2,3 , reduces the number of false positives to 14 and 8, respectively, with one additional true positive lost. Setting n=2 results in 13 out of 14 true positives (all except terphenyl) passing the screening, but with a high number (34) of false positives. Setting n=3 is not beneficial because the same 13 true positives pass the screening (terphenyl is still misclassified) along with 45 false positives. One may define the screening thresholds depending on their tolerance for false positives. With the thresholds set to one RMSE, the workflow presented here effectively eliminates most of the materials that are not of interest, which significantly reduces the number of candidates that need to be evaluated using more accurate and computationally expensive methods. Training better models for predicting the singlet exciton binding energy would require acquiring more GW +BSE data for materials with small exciton binding energies. To this end, the present models could be used to select materials for data acquisition.