To our knowledge, the only currently available open-source package to compute the similarity between Synthetic Routes is the one developed by Genheden et al. using the APTED algorithm . We provide a comparison between that approach and the one described here. However, it is worth noticing that the absence of a reference ground truth for synthetic routes similarity does not allow one to determine whether one implementation is more accurate than the other. We took 100 routes among those used by Genheden et al. and we computed the distance matrix with both the APTED and the NetworkX algorithms using the monopartite representation (chemical reaction only) of the routes. To make the results comparable, we used a set of parameters for the chemical similarity calculations analogous to the one used by Genheden et al.: difference fingerprints for chemical reactions, Morgan fingerprints for the molecules, and the Tanimoto algorithm for the similarity. Figure shows a good correlation between the values computed with the two algorithms, although the APTED values are generally lower than those calculated with NetworkX, and the agreement decreases as the values increase, suggesting a variance accumulation. On the one hand, the difference in the absolute values could depend on the actual algorithm. Also, in the approach by Genheden et al., some heuristics were imposed over the APTED algorithm to compensate for the fact that the routes are not ordered trees. On the other hand, small differences in the RDKit parameters used lead to different values of chemical similarity and, in turn, to different values of GED. Even though the absolute values are not identical, the overall Spearmen correlation between the two sets of APTED and GED values is good, namely 0.87. For what concerns computational efficiency, the algorithm by Genheden et al. outperforms the NetworkX GED, being much faster in computing the distance matrix. As shown in Figure , both algorithms have an approximately linear dependency on the number of routes. Still, the slope for the APTED approach is about two orders of magnitude smaller than the one for the GED NetworkX. The performance of the NetworkX algorithm can be improved by using the parallelization option for calculating the distance matrix available in LinChemIn. Clustering LinChemIn allows to cluster synthetic routes based on their similarity matrix. Currently, two clustering algorithms are available: Agglomerative Clustering as implemented in sklearn, and Hdbscan . Since the agglomerative clustering algorithm needs the number of clusters as input, we followed the strategy of Genheden et al. and implemented a process to optimize this parameter based on the Silhouette score. The default linkage is single, but users can modify it according to their needs. The 'clustering' module has a factory structure to enable scientists to experiment with other clustering algorithms.