We will start with making an analysis of the overall search timings, and we will start with the predictions on the set-n1 routes. The retrosynthesis search was performed until 500 iterations had elapsed for MCTS and Retro*, but 750 iterations for DFPN. The reason for this is that much fewer retrosynthesis tasks, i.e. call to one-step model and template application, are done each iteration. For MCTS, the rollout implementation ensures that several calls to the one-step model and template applications are performed at each iteration. For Retro*, only a single call to the one-step model is performed each iteration. But on the other hand, one sub-tree is added to the search tree for each applicable template suggested by the one-step model, requiring several template applications. The DFPN implementation also only calls the one-step model once per iteration, but contrary to Retro* only a single sub-tree is added to the search tree, the one for the most promising template. These implementation differences are reflected in the average number of calls to the one-step model and the average number of template applications. MCTS makes on average 7 to 8 times as many calls to the one-step model as Retro* and DFPN, respectively, whereas Retro* and DFPN make 3 to 2 times as many template applications, respectively. However, more importantly, the difference in the amount of work performed in each iteration is reflected in the average search time shown in Table . The average search time is 303, 301, 347 s for MCTS, Retro* and, DFPN, respectively. Thus while using 1.5 times as many iterations, DFPN is not much slower than MCTS and Retro*, and the differences between the search methods are practically not important. Because of the relatively small amount of work done in each iteration, DFPN also requires more iterations to find solutions and the convergence in the number of solved targets is slower, as shown in Figure . For MCTS and Retro*, the fraction of solved target is converged at 100 iterations, and thereafter the fraction increases only very slowly. However, for DFPN it is unclear if the fraction of solved targets has converged to the same degree even after 750 iterations.