Abstract: Monte Carlo Tree Search (MCTS) has been found to be a weaker player than minimax in some tactical domains, partly due to its highly selective focus only on the most promising moves. In order to combine the strategic strength of MCTS and the tactical strength of minimax, MCTS-minimax hybrids have been introduced in prior work, embedding shallow minimax searches into the MCTS framework. This paper continues this line of research by integrating MCTS and minimax even more tightly into one rollout-based hybrid search algorithm, MCTS- $$\alpha \beta $$ . The hybrid is able to execute two types of rollouts: MCTS rollouts and alpha-beta rollouts, i.e. rollouts implementing minimax with alpha-beta pruning and iterative deepening. During the search, all nodes accumulate both MCTS value estimates as well as alpha-beta value bounds. The two types of information are combined in a given tree node whenever alpha-beta completes a deepening iteration rooted in that node—by increasing the MCTS value estimates for the best move found by alpha-beta. A single parameter, the probability of executing MCTS rollouts vs. alpha-beta rollouts, makes it possible for the hybrid to subsume both MCTS as well as alpha-beta search as extreme cases, while allowing for a spectrum of new search algorithms in between. Preliminary results in the game of Breakthrough show the proposed hybrid to outperform its special cases of alpha-beta and MCTS. These results are promising for the further development of rollout-based algorithms that unify MCTS and minimax approaches.