This thesis compares four relevance feedback methods. The Rocchio and Ide dec-hi algorithms for the vector space model and the binary independence model and Okapi BM25 within the probabilistic framework. This is done in a custom-made Information Retrieval system utilizing a collection containing 131 896 LA-Times articles which is part of the TREC ad-hoc collection. The methods are compared on two grounds, using only the relevance information from the 20 highest ranked documents from an initial search and also by using all available relevance information. Although a significant effect of choice of method could be found on the first ground, post-hoc analysis could not determine any statistically significant differences between the methods where Rocchio, Ide dec-hi and Okapi BM25 performed equivalent. All methods except the binary independence model performed significantly better than using no relevance feedback. It was also revealed that although the binary independence model performed far worse on average than the other methods it did outperform them on nearly 20 % of the topics. Further analysis argued that this depends on the lack of query expansion in the binary independence model which is advantageous for some topics although has a negative effect on retrieval efficiency in general. On the second ground Okapi BM25 performed significantly better than the other methods with the binary independence model once again being the worst performer. It was argued that the other methods have problems scaling to large amounts of relevance information where Okapi BM25 has no such issues. / Uppsatsnivå: D