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Sponsored search has become increasingly important due to the rapid development of Web search engines and pay per click (PPC) is amongst one of the most important advertising models search engines currently use. One of the key questions in sponsored search is that: Given a query or a substituted keyword, which ads should search engines display to the users in order to maximize their revenue? In other words, given a keyword, how can we choose ads out of a candidate list that will have higher click-through rates (CTR)? Previous works have attempted to estimate the CTR of ads via a query-independent perspective. In this paper, instead of predicting the CTR of ads, we will propose a new ranking-based approach to select ads that would have higher click-through rates via a query-dependent perspective. We first analyze some manually constructed heuristic rules that could be used to distinguish good ads from bad ones and then show how we could combine these rules into our ranking-based approach to reach our aim. Experiments on real-world datasets have confirmed the effectiveness of our proposed approach.