Optically active nanomaterials are versatile building blocks for sensing approaches. Typically, they possess a high photostability. As photobleaching can be excluded, these material enable testing of many conditions and become interesting for for high throughput approaches. To turn a material into a sensor, the surface is commonly modified to selectively interact with an analyte (Figure , see also section 2). The optical properties as well as chemical tunability ask for large-scale sampling of the physical and chemical design space. In the simplest case, nanomaterials with different shapes, sizes, and/or compositions are separately modified with different surface modifications. Subsequently, different target analytes are added, and their optical response is monitored to select suitable combinations for further optimization (Figure ). Using such a format, it is possible to understand molecular recognition of different hybrid nanomaterials and identify combinations that are not selective on their own but form through their interaction with selective binding pockets. When available, it is possible to include rational design, chemical intuition, and computational predictions as guidelines for an efficient sampling. However, these principles can be difficult to apply for nanomaterials as the combinations are limitless and the interactions on the surface of a material can be complex. For example, SWCNT-based sensors are often modified with single stranded DNA. A typical 30 base long oligo nucleotide allows 4 different permutations. When confined to a nanomaterial, DNA behaves significantly different and forms hydrogen bonds that would be not typical in solution. Moreover, there are multiple oligonucleotides on a single SWCNT e.g. ≈ 265 (GT)15-oligonucleotides, which further increases the number of possible permutations to (4 ) . A small panel of such sensors with different interfaces (corona phases) can be synthesized and tested against a panel of different analytes.