We present the design and retrieval methodology of an intuitively operated retrieval assistant (RA) which supports the thematic search in databases of scientific libraries. The retrieval assistant establishes innovative and more adequate means for expressing a user's search interest by adopting aggregation operators of natural language (e.g. almost all, as many as possible), the interpretation of which is accomplished by novel methods from fuzzy set theory. These operators can be used in their intuitive meaning, i.e. just as in everyday language, for aggregating over sets of weighted search terms. The required scalability of the system is ensured through its multi-tier architecture, which disburdens both the clients and the external database servers by introducing an (arbitrarily replicable) intermediary to perform the computationally intensive aggregation step.