Enzymes are proteins that speed up chemical reactions. They power all living organisms, and are also incredibly useful for human purposes. Biochemists dream of having made-to-order enzymes for any reaction. Directed evolution is great at engineering enzymes, but needs starting points. De novo design can potentially create starting points with new-to-nature functions, but nobody has compared natural sequence mining and de novo design side-by-side on the same target reaction. Can design succeed where nature fails? Piperidines are a 6-atom ring common in medicines. Enzymes could help produce them more cheaply. Screening 188 natural enzymes found 0 with high specificity to piperidines. In one round of design with AP Novo we achieved higher piperidine specificity than any natural enzyme. DEHP is a plastic additive and toxic pollutant. Some natural enzymes can degrade it but are very rare. We designed DEHPases and found they were still active in 75% acetonitrile or 90C, which destroys most enzymes. This could be useful in harsh industrial processes. We also push the envelope on model reaction rates. Recent advances in de novo enzyme design are amazing, but high rates usually still require design iteration. How fast an enzyme can you get "straight from the computer" by maxing out in silico filters on previous data? We made a Kemp eliminase with kcat/Km=14,600 M-1s-1 and serine esterase on 4MU-Ac with kcat/Km=360,000 M-1s-1 and kcat=5/s, both without redesigning previous designs. This is SoTA "novel scaffold" (not strictly zero-shot because we iterated on filters). AP Novo is a generator-MPNN-AF3-filter pipeline. The generator does seq-struct co-diffusion via a simple transformer and no protein-specific inductive biases. It is SoTA on in silico benchmarks but for wetlab success, filters do the heavy lifting. Our key filter scores a backbone by redesigning multiple sequences on it and aggregating over the ensemble. You can think of this as measuring preorganization of catalytic geometry due to a backbone rather than a single design sequence. We think this idea, which is related to methods like NISE, ProteinHunter, Halludesign, RFOptimize, beam search, and iterative MPNN/partial diffusion, will be key to improving generation and evaluation going forward. We are excited by our results, but of course there are limitations: our best esterase borrows a natural structural scaffold; backbone-level preorganization does not get all the way to high natural-like activities; input motif can matter more than generator/filters. More research is needed! Please try the model and tell us what you think, and let us know if you are working on a hard problem where we could help!