The main goal of this paper is to describe and evaluate different indexing and stemming strategies for the Farsi (Persian) language. For this Indo-European language we have suggested a stopword list and a light stemmer. We have compared this stemmer to indexing strategy in which the stemming procedure was omitted, with or without stopword list removal, another publically available stemmer for this language as well as language independent n-gram indexing strategy. To evaluate the suggested solutions we used various IR models, including Okapi, Divergence from Randomness (DFR), a statistical language model (LM) as well as two vector space models, the classical tf idf and Lnu-ltc model. We have found that the Divergence from Randomness paradigm tends to propose better retrieval effectiveness than the Okapi, LM or vector-space models, the performance differences were however statistically significant only with the last two IR approaches. Ignoring the stemming ameliorates the MAP by more than 7%, giving the differences that are most of the time statistically significant. Finally, not removing the stoplist words for this language deprecates the MAP performance by 3%.