The traditional approach to supramolecular catalysts focuses on the design of macromolecular receptor with appropriately placed catalytic functional groups.  These catalysts are often inspired by the structure of enzymes with the catalytic group mimicking reactive amino acid residues, but unlike real enzymes, the binding sites of these catalysts are rigid structure made from chemical building blocks. All of the examples in this article are developed via the design approach.
Jeremy Sanders pointed out that the design approach has not been successful and has produced very few efficient catalysts because of rigidity of the supramolecules. He argued that rigid molecules with a slight mismatch to the transition state cannot be an efficient catalyst. Rather than investing so much synthesis effort on one rigid molecule that we cannot determine its precise geometry to the sub-angstrom level which is required for good stabilization, Sanders suggested the use of many small flexible building blocks with competing weak interactions so that it is possible for the catalyst to adjust its structure to accommodate the substrate better. There is a direct trade-off between the enthalpic benefit from flexible structure and the entropic benefit from rigid structure. Flexible structure could perhaps bind the transition state better but it allows more room for the substrates to move and vibrate. Most supramolecular chemists in the past prefer to build rigid structures out of fear of entropic cost.
This problem could perhaps be mended by Baker and Houk's "inside-out approach" which allows a systematic de novo enzyme development. This computational method starts simply with a predicted transition state structure and slowly builds outward by optimizing the arrangement of functional groups to stabilize the transition state. Then it fills out the remainder of the active site and, finally, it generates an entire protein scaffold that could contain the designed active site. This method could potentially be applied to supramolecular catalysis, although a plethora of chemical building blocks could easily overwhelm the computational model intended to work with 20 amino acids.