We introduce a test to assess mutual funds’ “conditional” performance that is based on updated information and corrects data snooping bias. Our method, named the functional false discovery rate “plus” ( $ fFDR^+ $ ), incorporates fund characteristics in estimating fund performance free of data snooping bias. Simulations suggest that the $ fFDR^+ $ controls well the ratio of false discoveries and gains considerable power over prior methods that do not account for extra information. Portfolios of funds selected by the $ fFDR^+ $ outperform other tests not accounting for information updating, highlighting the importance of evaluating mutual funds from a conditional perspective.