Predicting outcomes of chemical reactions, such as their yield based on data gathered in high throughput screening, is an important task in machine learning for chemistry. Previously, we were able to show that relatively simple fingerprintbased gradient boosting models can perform at least as well as computationally expensive DFT and transformer-based methods . In cheminformatics, chemical reactions are often defined as two sets of molecules, reactants and products, where the reactants are fully or partially transformed into the products during the reaction process. This set-based definition of chemical reactions hints at a potential use for set representation learning. Again, we focused on the most straightforward implementation by creating a dual-set neural network, where the inputs are binary ECFP (with r = 3 and including stereochemistry) vectors of reactant and product molecules, respectively. We evaluated this architecture, denoted MSR2-RXN, against the state-of-the-art using a high throughput data set of Buchwald-Hartwig cross-coupling reactions with the task of predicting reaction yields, that is, the percentage of input material (reactants) that is transformed to output materials (product). The baseline models include our previously introduced gradient boosting and fingerprint-based method (DRFP), a DFT-based random forests model (DFT), as well as the transformer-based models Yield-BERT and its augmented variant (Yield-BERT and Yield-BERT (aug.)) .