Exploring digital libraries of scientific articles is an essential task for any research community. The typical approach is to query the articles' data based on keywords and manually inspect the resulting list of documents to identify which papers are of interest. Besides being time-consuming, such a manual inspection is quite limited, as it can hardly provide an overview of articles with similar topics or subjects. Moreover, accomplishing queries based on content other than keywords is rarely doable, impairing finding documents with similar images. In this paper, we propose a visual analytic methodology for exploring and analyzing scientific document collections that consider the content of scientific documents, including images. The proposed approach relies on a combination of Content-Based Image Retrieval (CBIR) and multidimensional projection to map the documents to a visual space based on their similarity, thus enabling an interactive exploration. Additionally, we enable visual resources to display complementary information on selected documents that uncover hidden patterns and semantic relations. We show the effectiveness of our methodology through two case studies and a user evaluation, which attest to the usefulness of the proposed framework in exploring scientific document collections.