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Secondary metabolites produced by fungi and bacteria are among the potential agents that contribute to adverse health effects observed in occupants of buildings affected by moisture damage, dampness and associated microbial growth. However, few attempts have been made to assess the occurrence of these compounds in relation to moisture damage and dampness in buildings. This study conducted in the context of the HITEA project (Health Effects of Indoor Pollutants: Integrating microbial, toxicological and epidemiological approaches) aimed at providing systematic information on the prevalence of microbial secondary metabolites in a large number of school buildings in three European countries, considering both buildings with and without moisture damage and/or dampness observations. In order to address the multitude and diversity of secondary metabolites a large number of more than 180 analytes was targeted in settled dust and surface swab samples using liquid chromatography/mass spectrometry (LC/MS) based methodology. While 42%, 58% and 44% of all samples collected in Spanish, Dutch and Finnish schools, respectively, were positive for at least one of the metabolites analyzed, frequency of detection for the individual microbial secondary metabolites - with the exceptions of emodin, certain enniatins and physcion - was low, typically in the range of and below 10% of positive samples. In total, 30 different fungal and bacterial secondary metabolites were found in the samples. Some differences in the metabolite profiles were observed between countries and between index and reference school buildings. A major finding in this study was that settled dust derived from moisture damaged, damp schools contained larger numbers of microbial secondary metabolites at higher levels compared to respective dust samples from schools not affected by moisture damage and dampness. This observation was true for schools in each of the three countries, but became statistically significant only when combining schools from all countries and thus increasing the sample number in the statistical analyses.