This approach is brute forcing the search space restricted by computational limits. Alpha-Beta is just an optimization to minmax and not a fundamental change in the search approach.
Chess and Go are very different games and need to be treated as such. Lots of the recent excitement about Go is due to the innovations in machine learning and specifically deep learning that have been discovered. Particularly their performance on such a complex game - significantly more complex than chess. It was predicted that Go wouldn't be able to match top strength professionals for another 5 to 10 years.
AlphaGo Played Lee Sedol last year (considered one of the top players of the past decade) and won.[1] AlphaGo beating this prediction by so much was a large part of the excitement for last year's event.
The reason for the excitement was demonstrating that Monte Carlo search, which was originally dismissed as inferior, was a significantly better approach for the game of Go. Monte Carlo is a probabilistic based approach that uses random sampling - this is very different from the DFS approach used in chess.[2] It has proven be to very effective and without another breakthrough will almost certainly be the option used going forward.
[0] - https://en.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol
Both board evaluation and tree pruning are more difficult in Go. In Chess, capturing material is very important, but capturing stones in Go is often useless because the stones are in a bad position ("dead"). You can't decide who's winning just by counting captures. It's very difficult to hand code a board evaluation function, but AlphaGo's neural network solved the problem.
However, the neural network alone, with no tree search, was only enough to reach strong amateur level. Unlike Chess, where moves usually have consequences in the near future, a move in Go can be important tens or even hundreds of moves later. Alpha-beta pruning doesn't handle very distant consequences well, but Monte Carlo tree search does much better. Combined with the neural network it was enough to beat humans.
[0] https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning