Overall, the model using SDF embedding seems to be the best-performing model. One reason to explain the performance difference between the methods could lie in the architecture of the embeddings. SDF is the simplest reaction representation, and thus less likely to be overfit or to learn unnecessary patterns. One only needs to add and subtract the fingerprints of the molecules. DRFP reaction embedding is similar to SDF as it is also built to represent structural change that happens in a reaction. However, it involves detecting the sets circular structure in the molecules and hash the difference between those sets, thus being more complex than SDF. RXNFP uses a pre-trained BERT model to predict the embeddings. These complex structures might have worked against the model in this case leading to overfitting. Thus, the predicted route towards the bis-amide shown in Figure (molecule 23, route 2) is plausible but would benefit from using the protected version of the piperidine-2-carboxylic acid instead of the unprotected one. For one out of 50 molecules, the model failed to either predict the experimentally reported route or suggest a good or plausible alternative as the top-ranked prediction (Figure , molecule 8, route 5). Here, the synthesis strategy has to be significantly changed, and both steps of the current route are likely to fail. and thus require further optimization of conditions. Also, the use of dichloromethane as an alkylating agent seems non-optimal and the use of an alternative reactant can be beneficial. 24