In other related work, an Experience Guidance Network has been proposed to enhance the traditional MCTS search tree. It uses a network trained with synthetic 'experience' collected during the search process. This in turn aids in the evaluation of reaction templates, allowing focus on more promising routes. The method required fewer iterations on average to find a route than the Retro* method, and the generated routes were of higher quality in terms of route lengths and number of successful routes. Another approach integrated MCTS with A* search for retrosynthetic planning, to leverage the exploratory strength of MCTS with the goal-driven efficiency of A*. The method applied the rollout capabilities of MCTS into A*, allowing more focused and efficient pathway determination by evaluating future states more accurately. The study reported significant improvements in identifying routes, particularly for natural products. A success rate of 96% was achieved on the USPTO dataset.