Abstract Monte Carlo tree search has brought signifificant improvements to the level of computer players in games such as Go, but so far it has not been used very extensively in games of strongly imperfect information with a dynamic board and an emphasis on risk management and decision making under uncertainty. In this paper we explore its application to the game of Kriegspiel (invisible chess), providing three Monte Carlo methods of increasing strength for playing the game with little specifific knowledge. We compare these Monte Carlo agents to the strongest known minimax-based Kriegspiel player, obtaining signifificantly better results with a considerably simpler logic and less domain-specifific knowledge