Monte Carlo tree search is any form of Monte Carlo search applied to trees. For example a simple variant of depth search could randomly choose branches to follow, stopping when the path gets to a leaf, or, in the case of very large trees, at a random point before (getting bored!). As with any Monte Carlo techniques, heuristics can be used to assign probabilities to different branches. AlphaGo used Monte Carlo tree search alongside heuristics generated by deep neuaral networks.