We randomly selected n routes from each patent and then we performed an overlap check: no route should have leaf molecules that exist in another route as intermediate precursors (non-leaves), and the target molecule should not exist as an intermediate in another route. The motivation behind this check will be clear below. We then discarded all routes with a depth of more than 10 reactions, to exclude a few really long routes that will require an extensive search. For the non-overlapping routes with a depth of at most 10 reactions, we selected the 10,000 most diverse routes: the pair-wise distance matrix was computed using the machine learning approach previously described and then a greedy search with maxmin criteria was used to select the routes. We created two such sets of 10,000 routes one where n = 1 and one where n = 5, and we will refer to these two sets as set-n1 and set-n5, respectively. The distributions of the number of leaves, molecules, reactions, and longest linear route (LLR) are shown in Figure . For set-n1, the distributions are closer to the full set of routes: the number of molecules in the routes are typically small and the number of longer routes is quite small. On the contrary, in set-n5, the distributions are shifted to the right and there are more of the longer routes. There are 3.0% and 6.9% of convergent routes in set-n1 and set-n5, respectively.