This paper explores an effective method for retrieving additional images of a specific individual from large, unannotated photo collections using a reference query image. This task, known as face image retrieval (FIR), focuses on maximising recall by increasing the proportion of correct matches and improving precision by retrieving a greater number of relevant images. However, precision and recall inherently contradict each other, meaning that an increase in one often leads to a decrease in the other. To enhance the retrieval process, two key improvements are introduced. First, the existing consensus ranking method is strengthened to perform more reliably and faster. Second, it is discovered that mirroring the query image and averaging the corresponding face feature vectors leads to an overall improvement in both precision and recall. The proposed method is evaluated using both a historical photo dataset derived from criminal identification photos and a publicly available dataset for face recognition. Additionally, the efficiency of the CVLFace pipeline is examined, further assessing the overall effectiveness of the approach. These refinements contribute to a more robust approach for searching unannotated face photo databases, ensuring more effective retrieval performance.