A new Relevance Feedback (RF) technique is developed to improve upon the efficiency and performance of existing techniques. This is based on propagating positive and negative weights from documents judged relevant and not relevant respectively, to other documents, which are deemed similar according to one of a number of criteria. The performance and efficiency improve since the documents are treated as independent vectors rather than being merged into a single vector as is the case with traditional approaches, and only the documents considered in a given neighbourhood are inspected. This is especially important when using large test collections.