The ranking test also indicated the effectiveness of using embeddings. The embedded models were able to get the correct ranking more frequently compared to the no-embedding model. Among the three embedded models, SDF was the model that selected the true From the quantitative results in Table . During test time, SDF performed best in terms of MSE. Interestingly, the RXNFP and DRFP models both have much worse test performances compared to not using embeddings at all. This phenomenon is also observed in the R-squared metrics, where without embeddings, the model was able to explain about 66% of the variance in the data. On the other hands, the embedded models using DRFP or RXNFP only explained about 57 and 55% of the data variance, respectively. In this metric, again we see that SDF still significantly outperforms every other method with an R-squared value of 0.723, corresponding to 72% of the data variance being explained by this model. The final metric we considered is the Top-1 ranking accuracy. These metrics show how much of the top routes was the model able to recall. Different from the other two metrics, the model performed worse if not using embeddings. Only 44% of the best routes are correctly identified. On the other hand, DRFP and RXNFP perform better, at 50% and 49%, respectively. The best model is still SDF, which achieved an accuracy value of about 60%.