Discovering descriptions that highly distinguish a class label from another is still a chal- lenging task. Such patterns enable the building of intelligible classifiers and suggest hypothesis that may explain the presence of a label. Subgroup Discovery (SD), a framework that formally defines this pattern mining task, still faces two major issues: (i) to define appropriate quality measures characterizing the singularity of a pattern; (ii) to choose an accurate heuristic search space exploration when a complete enumeration is unfeasible. To date, the most efficient SD algorithms are based on a beam search. The resulting pattern collection lacks however of di- versity due to its greedy nature. We propose to use a recent exploration technique, Monte Carlo Tree Search (MCTS). To the best of our knowledge, this is the first attempt to apply MCTS for pattern mining. The exploitation/exploration trade-off and the power of random search leads to any-time mining (a solution is available any-time and improves) that generally outperforms beam search. Our empirical study on various benchmark and real-world datasets shows the strength of our approach with several quality measures.