[mtgp1000]

>But, rather than an irrelevant "bitter" lesson about how big machines can perfom more computations than a human, a really useful lesson -and one that we haven't yet learned, as a field- is why humans can do so well without search

I think the answer is heuristics based on priors(e.g. board state), which we've demonstrated (with alphago and derivatives, especially alphago zero) that neural networks are readily able to learn.

This is why I get the impression that modern neural networks are quickly approaching humanlike reasoning - once you figure out how to

(1) encode (or train) heuristics and

(2) encode relationships between concepts in a manner which preserves a sort of topology (think for example of a graph where nodes represent generic ideas)

You're well on your way to artificial general reasoning - the only remaining question becomes one of hardware (compute, memory, and/or efficiency of architecture).