This paper empirically investigates how marketers can retarget consumers who have searched online but did not purchase, based on their search behaviors. To infer the relationship between search activities and preferences, we estimate a structural search model that characterizes the consumer search process. We propose an estimator similar to the Geweke-Hajivassiliou-Keane estimator to evaluate the likelihood function. The proposed estimator makes recursive draws from truncated distributions that arise because of the observed search and choice behaviors in an optimal sequential search model. The recovered preferences are used to improve retargeting strategies demonstrated through a series of counterfactuals. Results show a substantial heterogeneity in responses to retargeting among consumers who exhibited different search behaviors. By contrast, the heterogeneity among consumers based on other characteristics (e.g., age, gender) is moderate. We consider two counterfactual marketing strategies: sending out coupons redeemed upon purchasing and sending seller recommendations that reveal the offering of recommended sellers. We find that although both strategies help increase the conversion rate, seller recommendations are more effective than coupons, suggesting the importance of providing consumers with the sellers’ information for retargeting. We also show that a pricing mechanism such as an auction that makes the seller self-select to participate will improve the effectiveness of retargeting. Finally, online retail platforms can benefit both sellers and consumers by providing sellers with the information on consumers’ search behaviors.