We construct Environmental, Social, and Governance (ESG) scores for U.S. active equity mutual funds using roughly 500 metrics across ESG issues and rating agencies. Under a revealed-preference framework, we use machine learning to identify issues driving fund flows and generate ESG-driven flows as proxies for ESG performance. We estimate expected benchmark-adjusted returns using ex-post realized returns and ex-ante measures. Better-ESG funds attract larger subsequent flows and face fewer redemptions after poor performance, yet have lower expected returns, indicating flows reflect investor ESG preferences rather than high-return beliefs. Investors effectively pay $48 million more annually for a top-ESG fund.