Much of the rare-event simulation literature is concerned with the development of asymptotically optimal algorithms. Because of the difficulties associated with applying these ideas to complex models, this paper focuses on sub-optimal procedures that can be shown to be much more efficient than conventional crude Monte Carlo. We provide two such examples, one based on ‘‘repeated acceptance/rejection’’ as a means of computing tail probabilities for hitting time random variables and the other based on filtered conditional Monte Carlo.