To illustrate the usage of PaRoutes, we have carried out route prediction experiments in order to benchmark three different search algorithms implemented in the AiZynthFinder software. We can draw some conclusions from these experiments: our implementation of DFPN is clearly inferior to MCTS and Retro*. DFPN finds fewer routes, solves fewer targets, and recovers to a lesser degree the reference routes. Further investigations are necessary to find the root cause of this, but it is clear in its current implementation, DFPN is not recommended. It should be mentioned that there are several implementations of proof-number search suggested in the literature, but all except one was designed to find a solution and then stop. The comparison of MCTS and Retro* is less clear-cut: both search algorithms solve roughly the same number of targets in roughly the same time. With respect to route quality, MCTS slightly outperforms Retro* especially for the set-n1 routes. Because both algorithms find approximately the same number of targets this shows that Retro* finds different solutions compared to MCTS. Which route would be the most chemically feasible is impossible to determine with certainty without doing experiments. However, we argue that any route significantly different from the reference route is likely inferior because the reference routes are extracted from patents, which naturally incorporate human thinking in the selected disconnections and in what order they are performed. It is worth emphasizing that all methods struggled with the set-n5 routes. Therefore, identifying and exploring human-like routes for complex targets that require longer synthetic routes seems to be an outstanding problem to solve.