Our most straightforward model, MSR1, performs as well as MPNN over all predicted properties (paired t-test, p = 0.529), as MPNN performs remarkably poorly on the HOMO and H-L tasks. On the other hand, both setbased models, MSR1 and MSR2, perform significantly worse than MPNN+ (paired t-test p < 0.001 and p < 0.002, respectively). These results suggest that adding explicit bonds and introducing message-passing does not significantly improve the predictive accuracy on the OCELOT data set, and in order to improve performance, additional molecular precomputed descriptors are needed; however, in practice, the choice of model may be influenced by the property of interest given the poor performance of MPNN on specific tasks, namely, HOMO and H-L. Over all properties, the set-enhanced model SR-GINE performs significantly better than GINE (paired t-test, p < 0.0001), yet not significantly different from the MPNNs (paired t-test, p < 0.138), although without the significant outliers observed when predicting To compare our set-based approach to a current state-of-the-art chemical large language model we finetuned the publicly available pretrained variant of MolFormer on OCELOT (see Section 4.6). Interestingly, the finetuned MolFormer performed below expectations compared to the MoleculeNet benchmark. A reason for the comparatively lower performance could be a lack of relevant samples in the original training set of the publicly available variant of MolFormer .