The vast amount of information generated by domain scientists makes the transitionfrom data to knowledge difficult and often impedes important discoveries. Forexample, the knowledge gained from protein flexibility data sets can speed advancesin genetic therapies and drug discovery. However, these models generate so muchdata that large scale analysis by traditional methods is almost impossible. This hindersbiomedical advances. Visual analytics is a new field that can help alleviate thisproblem. Visual analytics attempts to seamlessly integrate human abilities in patternrecognition, domain knowledge, and synthesis with automatic analysis techniques. Ipropose a novel, visual analytics pipeline and prototype which eases discovery, comparison,and exploration in the outputs of complex computational biology datasets.The approach utilizes automatic feature extraction by image segmentation to locateregions of interest in the data, visually presents the features to users in an intuitiveway, and provides rich interactions for multi-resolution visual exploration. Functionalityis also provided for subspace exploration based on automatic similarity calculationand comparative visualizations. The effectiveness of feature discovery and subspaceexploration is shown through a user study and user scenarios. Feedback from analystsconfirms the suitability of the proposed solution to domain tasks.