Overall, the comparative analysis shows that the SDF embedding consistently outperforms other approaches. However, an interesting finding emerged when comparing the different embedding approaches against the baseline noembedding model, highlighting an intriguing relationship between model complexity and performance. Notably, the more sophisticated embedding methods-DRFP and RXNFP-performed worse than even the baseline no-embedding model, with higher MSE and lower R-squared values. 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 the structural change that happens in a reaction. However, it involves detecting the set's circular structure in the molecules and hashing 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. This analysis suggests that in the context of route distance prediction, simpler embedding approaches may be more effective than more sophisticated ones. The SDF method's straightforward approach to capturing structural changes in reactions appears to provide the right balance of expressiveness and simplicity for this specific task.