COTS software products are widely developed on top of one or more OSS projects, which might lead to OSS reuse vulnerabilities. To discover such vulnerabilities, detecting OSS reuses for COTS software is a necessary step. Existing binary-to-source matching approaches are scalable to tens of thousands of OSS projects. However, when applying to COTS software products, they are suffering from precision problem severely due to their limited code features, imprecise matching score computation and the neglect of code structure of OSS projects. In this paper, we propose a novel binary-to-source matching approach B2SFinder to address these issues. It fully analyzes and selects seven kinds of code features that are presented in both binary file and source code and are not susceptible to compilation. In order to precisely calculate matching scores, it employs a weighted feature matching algorithm that combines three matching methods with two importance-weight computing algorithms. The matching methods are applied to different features according to the representation form of the features. The weighting algorithms compute the weight of a feature instance considering its specificity and occurrence frequency. B2SFinder further identifies the reuse type based on matching scores and code structures of OSS projects. We have implemented a prototype of B2SFinder with optimized data structure. We evaluated it on 21991 binaries of 1000 popular COTS software products. The results showed that it is not only precise but also scalable. It identified up to 2.15 times as many reuse cases as the state-of-the-art approach while only took 53.85 seconds on average for a binary file. It also plays a major role in discovering OSS reuse vulnerabilities.