Increases in online searches have spurred the growth of search-based advertising services offered by search engines, enabling companies to promote their products to consumers based on search queries. With millions of available keywords whose clickthru rates and profits are highly uncertain, identifying the most profitable set of keywords becomes challenging. We formulate a stylized model of keyword selection in search-based advertising services. Assuming known profits and unknown clickthru

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... , we develop an approximate adaptive algorithm that prioritizes keywords based on a prefix ordering -sorting of keywords in a descending order of expected-profit-to-cost ratio (or "bang-per-buck"). We show that the average expected profit generated by our algorithm converges to near-optimal profits, with the convergence rate that is independent of the number of keywords and scales gracefully with the problem's parameters. By leveraging the special structure of our problem, our algorithm trades off bias with faster convergence rate, converging very quickly but with only near-optimal profit in the limit. Extensive numerical simulations show that when the number of keywords is large, our algorithm outperforms existing methods, increasing profits by about 20% in as little as 40 periods. We also extend our algorithm to the setting when both the clickthru rates and the expected profits are unknown.