Running several sub-optimal algorithms and choosing the optimal one is a common procedure in computer science, most notably in the design of approximation algorithms. This paper deals with one significant flaw of this technique in environments where the inputs are provided by selfish agents: such protocols are not necessarily incentive compatible even when the underlying algorithms are. We characterize sufficient and necessary conditions for such best-outcome protocols to be incentive compatible in a general model for agents with one-dimensional private data. We show how our techniques apply in several settings