I asked a question about importance vs rejection sampling: Importance sampling vs acceptance-rejection and why one would use rejection sampling when importance sampling should produce a lower variance because it doesn't outright drop any samples. A question linked was this one: What is the difference between Metropolis-Hastings, Gibbs, Importance, and Rejection sampling? which asks about a much broader range of simulation techniques and the only distinction between importance and rejection sampling I could discern (and was also pointed out) from the answer was that rejection sampling produces an i.i.d sample while importance sampling does not.
This is good to know, but I still am not able to use this information on my original question which was - what would be an actual scenario where rejection sampling would be better applicable than importance sampling. In almost all instances of simulation, I feel we end up finding some kind of average anyway. In fact, this statement was made by the professor who taught a course on simulation I took (except, he didn't say "almost").