Thanks, I think I see the answer now. Let me summarize to check: There's a conceptual tree of all possible games, with a move at each node. You run some random games to completion, giving sequences of moves from the root (starting position) to a leaf (game over). Then, from the current move, you explore the best of the "next" moves more thoroughly - by doing the same thing again, starting from each of those "next" moves. The benefit of running random games to completion is it gives (some) long-range vision.

I wondered whether those (relatively) sparse "tracer bullets" actually find whole "good" sections of the tree - or whether it's just the specific leaf nodes it found that were good. The answer is that, since it's being used to guide exploration, it must be good - there'd be no point using it as a heuristic otherwise. So, the existence of a win in a region of the tree is a partial predictor of other wins in that region.

I read up on the rules of Go after asking, and it makes sense: once you own some territory, you'll tend to keep it - so that if a sequence of moves leads to a win, all the prefixes of those moves will also be strong positions along the way.

So it's not brute force, but a predictive heuristic. Still kinda surprising that it works well. :-)