Here, we present an adjusted reward estimation approach that provides random initial and candidate sets with realistic absolute numbers of unrepresented exceptional materials as well as distinct ordinary materials to distract from them. Let 𝑋 , … , 𝑋 (𝑁) denote the initial data elements in increasing order of their target property or figure of merit values. Based on an estimate 𝛼 ̂ of the unrepresented fraction of top materials 𝛼 = #{𝑋 ∈ Ω: 𝑦(𝑋) ≥ 𝑦(𝑋 (𝑁) )}/𝑀 create: