But the story you're telling doesn't seem entirely compatible with your comment earlier in this thread.

The earlier comment was comparing experiments to "armchair reasoning", while the later comment was comparing experiments to "all prior knowledge". I think the typical case is:

Amount of evidence in "all prior knowledge" >> Amount of evidence in an experiment >> Amount of evidence from "armchair reasoning".

If Bob is known to be an excellent researcher, can we trust HARKing if it comes from him?

I would pay a little more attention, but not that much more, and would want an experimental confirmation anyway. It seems to be that the world is complex enough and humans model it badly enough (for the sorts of things academia is looking at) that past evidence of good priors on one question doesn't imply good priors on a different question.

(This is an empirical belief; I'm not confident in it.)

In your simplified model above, there's no possibility of a result that is "just noise" and not explained by any particular hypothesis.

I expect that if you made a more complicated model where each hypothesis $H$ had a likelihood $p (D ∣ H)$ , and $p (D ∣ H)$ was high for $N$ hypotheses and low for the rest, you'd get a similar conclusion, while accounting for results that are just noise.

I know nothing about relativity, but I imagine a big part of Einstein's contribution was his discovery of a relatively simple hypothesis which explained all the data available to him.

I agree that relativity is an example that doesn't fit my story, where most of the work was in coming up with the hypothesis. (Though I suspect you could argue that relativity shouldn't have been believed before experimental confirmation.) I claim that it is the exception, not the rule.

Also, I do think it is often a valuable contribution to even think of a plausible hypothesis that fits the data, even if you should assign it a relatively low probability of being true. I'm just saying that if you want to reach the truth, this work must be supplemented by experiments / gathering good data.

In other words, it feels like the task of searching hypothesis space should be factored out from the task of Bayesian updating.

Bayesian updating does not work well when you don't have the full hypothesis space. Given that you know that you don't have the full hypothesis space, you should not be trying to approximate Bayesian updating over the hypothesis space you do have.