We combine theory and empirics to (i) show that some buyers in online advertising markets are nancially constrained and (ii) demonstrate how to design auctions that take into account such nancial constraints. We use data from a eld experiment where reserve prices were randomized on Google’s advertising exchange (AdX). We nd that, contrary to the predictions of classical auction theory, a signicant set of buyers lowers their bids when reserve prices go up. We show that this behavior can be explained if we assume buyers have constraints on their minimum return on investment (ROI). We proceed to design auctions for ROI-constrained buyers. We show that optimal auctions for symmetric ROI- constrained buyers are either second-price auctions with reduced reserve prices or subsidized second-price auctions. For asymmetric buyers, the optimal auction involves a modication of virtual values. Going back to the data, we show that using ROI-aware optimal auctions can lead to large revenue gains and large welfare gains for buyers.