Aside from estimating the value of a node from similar nodes, another option is to reduce the search space by putting them in the same information set. This means that to one player they appear indistinguishable, so the same strategy is played in all of them, while the other players might still be able to distinguish them. If your game has a huge state space it's likely that some of the state just hardly matters some of the time to some of the opponents. Other methods of fusing states have the disadvantage that all players have to care about the same information.
To implement MCTS with information sets, use the Information Set MCTS algorithm (ISMCTS) [1], which is a pretty obvious and intuitive extension of MCTS. (Has nobody really described ISMCTS before?) Whitehouse's PhD thesis [2] goes into more detail, and looks at using ISMCTS to reduce the search space in games ("ICARUS"), though I didn't read that chapter. (Earlier this year I mostly-implemented ISMCTS for Cordial Minuet, a game with simultaneous moves in addition to imperfect information. Information sets are unavoidable in simultaneous-move games.)
Also, you will find loads of papers specifically discussing speeding up MCTS for Go, many of which generalise.
[1] http://eprints.whiterose.ac.uk/75048/1/CowlingPowleyWhitehou... [2] http://etheses.whiterose.ac.uk/8117/