We examine when and how to reward the bearer of bad news in a dynamic principal-agent relationship with experimentation. The agent receives flow rents from experimentation, and divides his time between searching for conclusive good news and conclusive bad news about project quality. The principal commits in advance to rewards conditional on the type of news. At each instant, the principal observes the agent’s allocation and news and makes a firing decision. We show that the principal’s optimal Markov perfect equilibrium features a stark reward structure: either the principal does not reward the bearer of bad news at all or rewards the bearer of either news equally.