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Biodegradability is fundamental to the assessment of environmental exposure and risk from organic chemicals. Predictive models can be used to pursue both regulatory and chemical design (green chemistry) objectives, which are most effectively met when models are easy to use and available free of charge. The objective of this work was to evaluate no-cost estimation programs with respect to prediction of ready biodegradability. Fragrance materials, which are structurally diverse and have significant exposure potential, were used for this purpose. Using a database of 222 fragrance compounds with measured ready biodegradability, 10 models were compared on the basis of overall accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC), a measure of quality for binary classification. The 10 models were VEGA(C) Non-Interactive Client, START (Toxtree(C)), Biowin(C)1-6, and two models based on inductive machine learning. Applicability domain (AD) was also considered. Overall accuracy was ca. 70% and varied little over all models, but sensitivity, specificity and MCC showed wider variation. Based on MCC, the best models for fragrance compounds were Biowin6, VEGA and Biowin3. VEGA performance was slightly better for the <50% of the compounds it identified as having "high reliability" predictions (AD index >0.8). However, removing compounds with one and only one quaternary carbon yielded similar improvement in predictivity for VEGA, START, and Biowin3/6, with a smaller penalty in reduced coverage. Of the nine compounds for which the eight models (VEGA, START, Biowin1-6) all disagreed with the measured value, measured analog data were available for seven, and all supported the predicted value. VEGA, Biowin3 and Biowin6 are judged suitable for ready biodegradability screening of fragrance compounds. Published by Elsevier B.V.