While geographic search filters exist, few of them are validated and there are currently none that focus on Germany. We aimed to develop and validate a highly sensitive geographic search filter for MEDLINE (PubMed) that identifies studies about Germany. First, using the relative recall method, we created a gold standard set of studies about Germany, dividing it into 'development' and 'testing' sets. Next, candidate search terms were identified using (i) term frequency analyses in the 'development set' and a random set of MEDLINE records; and (ii) a list of German geographic locations, compiled by our team. Then, we iteratively created the filter, evaluating it against the 'development' and 'testing' sets. To validate the filter, we conducted a number of case studies (CSs) and a simulation study. For this validation we used systematic reviews (SRs) that had included studies about Germany but did not restrict their search strategy geographically. When applying the filter to the original search strategies of the 17 SRs eligible for CSs, the median precision was 2.64% (interquartile range [IQR]: 1.34%-6.88%) versus 0.16% (IQR: 0.10%-0.49%) without the filter. The median number-needed-to-read (NNR) decreased from 625 (IQR: 211-1042) to 38 (IQR: 15-76). The filter achieved 100% sensitivity in 13 CSs, 85.71% in 2 CSs and 87.50% and 80% in the remaining 2 CSs. In a simulation study, the filter demonstrated an overall sensitivity of 97.19% and NNR of 42. The filter reliably identifies studies about Germany, enhancing screening efficiency and can be applied in evidence syntheses focusing on Germany.