Content-based Image Retrieval (CBIR) systems have been rapidly developing over the years, both in labs and in real world applications. Face Image Retrieval (FIR) is a specialised CBIR system where a user submits a query (image of a face) to the FIR system which searches and retrieves the most visually similar face images from a database. In this paper, we use a neural-network based similarity measure and compare the retrieval performance to Lp-norm similarity measures. Further we examined the effect of user relevance-feedback on retrieval performance. It was found that the neural-similarity measure provided significant performance gains over Lp-norm similarity measures for both the training and test data sets.