When can a feature independence assumption be reasonable and when not?

For example, Naïve Bayes assumes that the features are conditionally independent and they perform really well. Is there a time when assuming features are conditionally independent not so reasonable? And why would we want to do that?

Would we consider that when we want simple training model and not want it to take high computation power?