Decision makers conducting costly experiments often seek advice from experts with a stake in the outcome: executives evaluating competing product designs or policymakers designing new regulation consult specialized engineers or industry lobbyists who have frontier expertise. I study how a decision maker experimenting across multiple prospects may interact productively with a biased expert who strategically discloses verifiable evidence about each prospect. The expert favors a subset of prospects, regardless of their viability for the decision maker, and neither player can commit to future behavior. The order of experimentation shapes the expert's incentives: testing favored prospects earlier can induce disclosure about alternatives if experimentation continues. Pareto-efficient equilibria feature disclosure in batches, generally with delay. Between disclosures, the decision maker experiments optimally according to an adjusted Gittins index. Under refinements that rule out implausible disclosure behavior, optimal experimentation follows an endogenous index ordering across all prospects. Comparative statics highlight that faster learning or greater alignment with the expert need not improve the decision maker's welfare.