Spontaneously retrieving analogies from presented problem data is an important phase of analogical reasoning, influencing many related cognitive processes. Existing models have focused on semantic similarity, but structural similarity is also a necessary requirement of any analogical comparison. We present a new technique for performing structure based analogy retrieval. This is founded upon derived attributes that explicitly encode elementary structural qualities of a domains representation. Crucially, these attributes are unrelated to the semantic content of the domain information, and encode only its structural qualities. We describe a number of derived attributes and detail the computation of the corresponding attribute values. We examine our models operation, detailing how it retrieves both semantically related and unrelated domains. We also present a comparison of our algorithms performance with existing models, using a structure rich but semantically impoverished domain.