Perhaps you could see trying to think of analogies as sampling randomly in conceptspace from a reference class that the concept you are interested in belongs to.

Imagine a big book of short computer programs that simulate real-life phenomena. I'm working on a new program for a particular phenomenon I'm trying to model. I don't have much data about my phenomenon, and I'm trying to figure out if a recursive function (say) would accurately model the phenomenon. By looking through my book of programs, I can look at the frequency with which recursive functions seem to pop up when modeling reality and adjust my credence that the phenomenon can be modeled with a recursive function accordingly.

Choosing only to look at pages for phenomena that have some kind of isomorphism with the one I'm trying to model amounts to sampling from a smaller set of data points from a tighter reference class.

This suggests an obvious way to improve on reasoning by analogy: try to come up with a bunch of analogies, in a way that involves minimal motivated cognition (to ensure a representative sample), and then look at the fraction of the analogies for which a particular proposition holds (perhaps weighting more isomorphic analogies more heavily).