Microblog has gained more and more users around the world, the popularity of which makes information spreading in microblog the most important and influential activities on the Internet. Therefore, search in microblog is of the most significant issue for both academic and industrial world. Search in webpages has been studied for several decades, but as for microblog it is still an open and brand new question for everyone. Search in microblog is more difficult than that in traditional webpages because of the sparseness of the messages. Search functions in current microblogging services simply match microblog messages with query words, which cannot guarantee the correlation between the retrieving messages and the users’ intention. We introduce the concept of conversational thread to gain more information and improve the search result in microblog. We also use SVMRank to train a model to determine the rank of relevance of the queries and messages. Through a series of experiments, we proved that our method is easy to implement, and can improve the precision up to 29% in average.