The Monte-Carlo Tree Search used in the AlphaZero may easily miss a critical move because it is based on sampling search space and focuses on the most promising moves. In addition, the Monte-Carlo Tree Search may sample a move for many times even if this move has been explored with a determined game-theoretical value. In this paper, we propose an Exact-win-MCTS that makes use of sub-tree’s information (WIN, LOSS, DRAW, and UNKNOWN) to prune unneeded moves to increase the opportunities of discovering the critical moves. Our method improves and generalizes some previous MCTS variations as well as the AlphaZero approach. The experiments show that our Exact-win-MCTS substantially promotes the strengths of Tic-Tac-Toe, Connect4, and Go programs especially. Finally, our Exact-win Zero defeats the Leela Zero, which is a replication of AlphaZero and is currently one of the best open-source Go programs, with a significant 61% win rate. Therefore, we are pleased to announce that our Exact-win-MCTS has overcome the AlphaZero approach without using extra training time, playing time, or computer resources. As far as we know, this is the first practical idea with concrete experiments to beat the AlphaZero approach.