OBJECTIVES IBS is often a diagnosis of exclusion, with poor diagnosis coding in primary care and identification of eligible research participants challenging. We present the methodology of an on-going multi-centre, observational, retrospective research study, designed to overcome the challenges of IBS patient identification. METHODS FARSITE, a software tool for identification of research participants developed by the Greater Manchester Comprehensive Local Research Network and North West eHealth, was used to screen anonymised primary care records for potentially eligible patients. Ethical approval reference 13/LO/0692. Search criteria: patients aged 18-60; combination READ code symptoms indicative of IBS and prescription of IBS medications 01/01/2009–31/12/2011. GPs at 8 participating practices in Salford & Greater Manchester reviewed clinical records of the FARSITE-generated list of patients to apply full eligibility criteria for final patient selection. Inclusion criteria: medical diagnosis of IBS or meeting ROME III criteria; provision of consent. Exclusion Criteria: diagnosis excluding IBS; IBS symptoms secondary to other condition; IBS medications only for non-GI symptoms. RESULTS FARSITE identified 1089 (1.3%) patients, of which 297 (27.3%) were eligible. 97 patients consented to participation (79% female). Main reasons for non-eligibility were not meeting ROME III criteria or IBS excluded by medical opinion. Patients were most commonly coded as irritable colon (37%), difficulty defecating (21%), abdominal pain (18%), diarrhoea symptoms (14%). Four (4%) patients had a READ code specific for IBS. The median (IQR) time from 1st presentation with abdominal symptoms to study eligibility was 3.98 (0.00-9.04) years. CONCLUSIONS Identification of patients with IBS using READ codes is sub-optimal in primary care. A combination search of READ codes with symptom and prescription data via FARSITE has enabled potential participants to be identified with a reasonable screening failure rate. FARSITE is a valuable research tool aiding study feasibility by reducing the need for manual patient identification.