Why do neural networks based AI players still require extensive search techniques? Today we have neural network based AI players that are comparable or better than humans in games that require extensive pattern matching and "intuition". AlphaGo is a prime example. 

But these AI players usually have both neural networks and search algorithms in place. Humans, on the other hand, rely just on the pattern matching and "intuition" (even the best chess players can see just a handful of moves ahead). 

So, why do AI players still require extensive search while humans don't? How would AIs like AlphaGo perform if we take the search part out?
It is not very accurate to say that AI players requires extensive search while humans don't. Rather, it is a question of degree. 

AIs do a lot more calculating, because that's what computers are good at. 

Human intuition is much more powerful than a neural network can currently hope to match, because it is much more integrated into a world of knowledge about the game, it uses orders of magnitudes more neurons and it is not a static thing that just provides a move.

But if a human player stops calculating ahead his playing strength will drop very significantly. This can be seen by assessing the performance in games with short time controls: The less time you have the less calculation is happening, your intuition however is fast.

If you take out the search component of AlphaGo it would still play quite strongly, probably at a low dan level. Of course that is also far below its strength. 

So, search is always an important component of playing strength, just more so for machines.