Related, I was talking to a computational chemist at a conference a few years ago. Their work was mostly at the intersection of ML and material science.

An interesting concept they mentioned was this idea of "injected serendipity" when they were screening for novel materials with a certain target performance. They proceed as normal, but 10% or so of the screened materials are randomly sampled from the chemical space.

They claimed this had led them to several interesting candidates across several problems.

A few month ago I went to a similar talk. They got a carboxylic acid from a plant (I forgot the name) that has some activity to kill caterpillar that eat corn, and made like 10 or 15 compounds with organic alcohols to get an ester. They tried different doses on the caterpillars and then make a computer model to predict the activity of similar compounds (QSAR). The idea is to use it in a long list of other organic alcohols and try to find a better compound.

But they choose chemical reactions that are usual in the lab, so they guess they will be able to make it work in the lab, and they keep most of the structure without changes. So it's closer to what they classify here as look nearby the known good points instead of a true random search.

That is an unrelated problem, that usually is not even a problem.

For molecules, 10 Armstrong away is probably as good as infinite.

For how many bananas should you eat per week to become the chess world champion, you can ask Wolfram Alpha to convert 2400kcal * 7 to bananas and get an upper bound.

I think everyone agree that with infinite time a resources a brute force search is better in case there is a weird combination. But for finite time and resources you need to select a better strategy unless the search space is ridiculous small and smooth.