Then, we evaluated how existing methods for yield prediction perform on NiCOlit. As the representation of chemical reactions in a machine readable format is a crucial step in statistical modeling of reaction yields 25 , we selected three approaches representative of the state-of-the-art (see section III of SI). The first approach, RDKit FingerPrint (RDKit FP), is based on the RDKit's (a cheminformatics tool) chemical reaction fingerprints and one-hot encoding of the remaining variables. The second approach, referred to as Density Functional Theory (DFT), follows the guidelines given by the Auto-QChem framework to generate DFT-based molecular descriptors adapted to the selected reaction. For the third approach, RXNFP, we featurized chemical reactions using the deep-learning RXNFP method . Yield prediction models were built for each featurization with Random Forest regression models that have shown excellent results on reaction yield prediction . All metrics reported are averaged over 10 experiments with different random seeds. The DFT method outperforms the two others, and reaches an R 2 of 0.54, even though the difference with the RDKit FP is modest (R 2 of 0.49), while RXNFP showed the weakest performance (R 2 of 0.37) (Fig. ). As expected, the performance of the model trained on optimization data performed better (R 2 of 0.48) than when trained on scope data (R 2 of 0.36) (Fig. ).