In our previous research, we investigated the properties of case-based plan recognition with incomplete plan libraries. Retrieval based on similarities among planning situations, rather than on similarities among planning actions, enables recognition in light of novel planning actions. In this paper we explore the benefits of predictions following a retrieval scheme that utilizes a similarity measure among the states in the abstract state-space, based on the k-nearest neighbor similarity metric. Such retrieval scheme may enable the recognition in light of newly observed abstract situations. Properties of the retrieval in abstract state-spaces are investigated in two different planning domains. Experimental results show that improvements in the recognition process depend on the characteristics of a given planning domain.