The OCELOT chromophore data set contains chemically diverse π-conjugated molecules, meaning that our set-based architectures should, according to our hypothesis, perform better than graph-based architectures as they put less emphasis on specific bonds . For this data set, we changed our hyperparameters to reduce the size of all our models to less than 100K parameters, as the relatively high number of samples and tasks, combined with the 5-fold cross-validation, requires non-trivial computational resources. The original study used the data set to train a hierarchy of models that follow increasingly complex architectures, peaking with a message-passing neural network for quantum chemistry .