Applying a "co-search" algorithm to Internet traffic at the SEC's EDGAR website, we develop a novel method for identifying economically related peer firms. Our results show that firms appearing in chronologically adjacent searches by the same individual (Search Based Peers or SBPs) are fundamentally similar on multiple dimensions. In direct tests, SBPs dominate GICS6 industry peers in explaining cross-sectional variations in base firms' out-of-sample (a) stock returns, (b) valuation multiples, (c) growth rates, (d) R&D expenditures, (e) leverage, and (f) profitability ratios. We show that SBPs are not constrained by standard industry classification and are more dynamic, pliable, and concentrated. Our results highlight the potential of the collective wisdom of investors--extracted from co-search patterns--in addressing long-standing benchmarking problems in finance.