Background and Context: Reports suggest plagiarism is a common occurrence in universities. While plagiarism detection mechanisms exist for textual artifacts, this is less so for non-code related ones such as software design artifacts like models, metamodels or model transformations. Objective: To provide an efficient mechanism for the detection of plagiarism in repositories of Model-Driven Engineering (MDE) assignments. Method: Our approach is based on the adaptation of the Locality Sensitive Hashing, an approximate nearest neighbor search mechanism, to the modeling technical space. We evaluate our approach on a real use case consisting of two repositories containing 10 years of student answers to MDE course assignments. Findings: We have found that: (1) effectively, plagiarism occurred on the aforementioned course assignments (2) our tool was able to efficiently detect them. Implications: Plagiarism detection must be integrated into the toolset and activities of MDE instructors in order to correctly evaluate students