Most search engines return a lot of unwanted information. A more thorough filtering process can be performed on this information to sort out the relevant documents. A new method called Frequency Domain Scoring (FDS), which is based on the Fourier Transform is proposed. FDS performs the filtering by examining the locality of the keywords throughout the documents. This is examined and compared to the well known techniques Latent Semantic Indexing (LSI) and Cosine measure. We found that FDS obtains better results of how relevant the document is to the query. The other two methods (cosine measure, LSI) do not perform as well mainly because they need a wider variety of documents to determine the topic.