This isn't right.

You can conduct cheap-and-easy experiments to verify the results, as opposed to imaging which doesn't always work anyway.

I don't quite understand this; what's an example of an experiment that verifies whether or not you predicted structure is accurate without determining the actual structure?

I mean, use the ML model to predict where they will be and then do an experiment to confirm it. My understanding from what I've read is that it's easier to make sense of experimental data (and likely cheaper) when you have a good idea what you're looking for.