  Computational approaches to exploring "chemical universes", i.e., very large
sets, potentially infinite sets of compounds that can be constructed by a
prescribed collection of reaction mechanisms, in practice suffer from a
combinatorial explosion. It quickly becomes impossible to test, for all pairs
of compounds in a rapidly growing network, whether they can react with each
other. More sophisticated and efficient strategies are therefore required to
construct very large chemical reaction networks.
  Undirected labeled graphs and graph rewriting are natural models of chemical
compounds and chemical reactions. Borrowing the idea of partial evaluation from
functional programming, we introduce partial applications of rewrite rules.
Binding substrate to rules increases the number of rules but drastically prunes
the substrate sets to which it might match, resulting in dramatically reduced
resource requirements. At the same time, exploration strategies can be guided,
e.g. based on restrictions on the product molecules to avoid the explicit
enumeration of very unlikely compounds. To this end we introduce here a generic
framework for the specification of exploration strategies in graph-rewriting
systems. Using key examples of complex chemical networks from sugar chemistry
and the realm of metabolic networks we demonstrate the feasibility of a
high-level strategy framework.
  The ideas presented here can not only be used for a strategy-based chemical
space exploration that has close correspondence of experimental results, but
are much more general. In particular, the framework can be used to emulate
higher-level transformation models such as illustrated in a small puzzle game.
