The PAH101 dataset was originally generated for the purpose of SF materials discovery. To address the challenge of con-structing transferable ML models with a small amount of training data, we used the sure-independence-screening-and-sparsifyingoperator (SISSO) 121,122 algorithm. SISSO takes advantage of physical/ chemical knowledge to train ML models based on "small data". The input of SISSO is a set of primary features, which are physical/ chemical descriptors thought to be related to the target property. The primary features used in Ref. are also provided in the PAH101 data records. 110 SISSO generates a huge feature space by repeatedly combining the primary features using a set of linear and nonlinear algebraic operations with rules to avoid unphysical combinations. Subsequently, linear regression is performed to identify the most predictive models. In the last few years SISSO has been applied increasingly broadly for diverse classes of materials and target properties and continues to perform well with relatively small amounts of data. In Ref. SISSO successfully produced several models that were able to predict the GW +BSE values of the SF driving force, which corresponds to the difference between the singlet exciton energy and twice the triplet exciton energy, with a root mean square error (RMSE) below 0.2 eV. Based on considerations of accuracy and computational cost, two of the SISSO models were selected to build a classifier for materials screening. Later, the simpler model of the two was found to deliver robust performance outside of the PAH101 set, whereas the more complex model was found to be overfitted. Here, we use the PAH101 dataset to train SISSO models for the singlet exciton energy, which corresponds to the optical gap, the triplet exciton energy, the singlet-triplet gap, and the singlet exciton binding energy. For all four properties, the best performing SISSO models yield predictions within about 0.2 eV of the GW +BSE reference values. We then select SISSO models based on considerations of accuracy and computational cost to construct materials screening workflows for each property. The screening targets are chosen to demonstrate typical use-cases relevant for organic electronic devices. We demonstrate screening for materials with an optical gap in a particular color; materials meeting the requirements for up-conversion of infrared light to the visible range via TTA, namely, a triplet excitation energy in the infrared, optical gap in the visible range, and a higher TTA driving force than rubrene; materials with a small singlet-triplet gap, which is desirable for OLEDs; and materials with a small singlet exciton binding energy to facilitate the separation of excitons into free charge carriers. We show that the workflows based on SISSO models can effectively screen out most of the materials that are not of interest and significantly reduce the number of candidates selected for further evaluation using computationally expensive excited-state theory.